diff --git a/python/tflite_micro/signal/BUILD b/python/tflite_micro/signal/BUILD
index a0f9ae1..742fd4e 100644
--- a/python/tflite_micro/signal/BUILD
+++ b/python/tflite_micro/signal/BUILD
@@ -16,7 +16,10 @@
     visibility = [":signal_friends"],
     deps = [
         ":delay_op_cc",
+        ":energy_op_cc",
         ":fft_ops_cc",
+        ":framer_op_cc",
+        ":overlap_add_op_cc",
         ":window_op_cc",
     ],
 )
@@ -31,7 +34,10 @@
     visibility = ["//python/tflite_micro/signal/utils:__subpackages__"],
     deps = [
         ":delay_op",
+        ":energy_op",
         ":fft_ops",
+        ":framer_op",
+        ":overlap_add_op",
         ":window_op",
     ],
 )
@@ -60,6 +66,32 @@
 )
 
 py_tflm_signal_library(
+    name = "energy_op",
+    srcs = ["ops/energy_op.py"],
+    cc_op_defs = ["//signal/tensorflow_core/ops:energy_op"],
+    cc_op_kernels = [
+        "//signal/tensorflow_core/kernels:energy_kernel",
+    ],
+)
+
+py_test(
+    name = "energy_op_test",
+    size = "small",
+    srcs = ["ops/energy_op_test.py"],
+    data = [
+        "//python/tflite_micro/signal/ops/testdata:energy_test1.txt",
+    ],
+    python_version = "PY3",
+    srcs_version = "PY3",
+    deps = [
+        ":energy_op",
+        "//python/tflite_micro/signal/utils:util",
+        requirement("numpy"),
+        requirement("tensorflow-cpu"),
+    ],
+)
+
+py_tflm_signal_library(
     name = "fft_ops",
     srcs = ["ops/fft_ops.py"],
     cc_op_defs = ["//signal/tensorflow_core/ops:fft_ops"],
@@ -86,6 +118,56 @@
 )
 
 py_tflm_signal_library(
+    name = "framer_op",
+    srcs = ["ops/framer_op.py"],
+    cc_op_defs = ["//signal/tensorflow_core/ops:framer_op"],
+    cc_op_kernels = [
+        "//signal/tensorflow_core/kernels:framer_kernel",
+    ],
+)
+
+py_test(
+    name = "framer_op_test",
+    size = "small",
+    srcs = ["ops/framer_op_test.py"],
+    data = [
+        "//python/tflite_micro/signal/ops/testdata:framer_test1.txt",
+    ],
+    python_version = "PY3",
+    srcs_version = "PY3",
+    deps = [
+        ":framer_op",
+        "//python/tflite_micro/signal/utils:util",
+        requirement("numpy"),
+        requirement("tensorflow-cpu"),
+    ],
+)
+
+py_tflm_signal_library(
+    name = "overlap_add_op",
+    srcs = ["ops/overlap_add_op.py"],
+    cc_op_defs = ["//signal/tensorflow_core/ops:overlap_add_op"],
+    cc_op_kernels = [
+        "//signal/tensorflow_core/kernels:overlap_add_kernel",
+    ],
+)
+
+py_test(
+    name = "overlap_add_op_test",
+    size = "small",
+    srcs = ["ops/overlap_add_op_test.py"],
+    python_version = "PY3",
+    srcs_version = "PY3",
+    deps = [
+        ":overlap_add_op",
+        "//python/tflite_micro/signal/utils:util",
+        "@absl_py//absl/testing:parameterized",
+        requirement("numpy"),
+        requirement("tensorflow-cpu"),
+    ],
+)
+
+py_tflm_signal_library(
     name = "window_op",
     srcs = ["ops/window_op.py"],
     cc_op_defs = ["//signal/tensorflow_core/ops:window_op"],
diff --git a/python/tflite_micro/signal/ops/energy_op.py b/python/tflite_micro/signal/ops/energy_op.py
new file mode 100644
index 0000000..1fd37e8
--- /dev/null
+++ b/python/tflite_micro/signal/ops/energy_op.py
@@ -0,0 +1,45 @@
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Use energy op in python."""
+
+import tensorflow as tf
+from tflite_micro.python.tflite_micro.signal.utils import util
+
+gen_energy_op = util.load_custom_op('energy_op.so')
+
+
+def _energy_wrapper(energy_fn, default_name):
+  """Wrapper around gen_energy_op.energy*."""
+
+  def _energy(input_tensor, start_index=0, end_index=-1, name=default_name):
+    with tf.name_scope(name) as name:
+      input_tensor = tf.convert_to_tensor(input_tensor, dtype=tf.int16)
+      dim_list = input_tensor.shape.as_list()
+      if len(dim_list) != 1:
+        raise ValueError("Input tensor must have a rank of 1")
+      if end_index == -1:
+        end_index = dim_list[0] - 1
+      return energy_fn(input_tensor,
+                       start_index=start_index,
+                       end_index=end_index,
+                       name=name)
+
+  return _energy
+
+
+# TODO(b/286250473): change back name after name clash resolved
+energy = _energy_wrapper(gen_energy_op.signal_energy, "signal_energy")
+
+tf.no_gradient("signal_energy")
diff --git a/python/tflite_micro/signal/ops/energy_op_test.py b/python/tflite_micro/signal/ops/energy_op_test.py
new file mode 100644
index 0000000..8df9514
--- /dev/null
+++ b/python/tflite_micro/signal/ops/energy_op_test.py
@@ -0,0 +1,137 @@
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for energy op."""
+import os
+
+import numpy as np
+import tensorflow as tf
+
+from tensorflow.python.platform import resource_loader
+from tflite_micro.python.tflite_micro.signal.ops import energy_op
+from tflite_micro.python.tflite_micro.signal.utils import util
+
+
+class EnergyOpTest(tf.test.TestCase):
+
+  _PREFIX_PATH = resource_loader.get_path_to_datafile('')
+
+  def GetResource(self, filepath):
+    full_path = os.path.join(self._PREFIX_PATH, filepath)
+    with open(full_path, 'rt') as f:
+      file_text = f.read()
+    return file_text
+
+  def SingleEnergyTest(self, filename):
+    lines = self.GetResource(filename).splitlines()
+    args = lines[0].split()
+    start_index = int(args[0])
+    end_index = int(args[1])
+
+    func = tf.function(energy_op.energy)
+    input_size = len(lines[1].split())
+    concrete_function = func.get_concrete_function(tf.TensorSpec(
+        input_size, dtype=tf.int16),
+                                                   start_index=start_index,
+                                                   end_index=end_index)
+    interpreter = util.get_tflm_interpreter(concrete_function, func)
+    # Skip line 0, which contains the configuration params.
+    # Read lines in pairs <input, expected>
+    i = 1
+    while i < len(lines):
+      in_frame = np.array([int(j) for j in lines[i].split()], dtype='int16')
+      out_frame_exp = [int(j) for j in lines[i + 1].split()]
+      # TFLM
+      interpreter.set_input(in_frame, 0)
+      interpreter.invoke()
+      out_frame = interpreter.get_output(0)
+      for j in range(start_index, end_index):
+        self.assertEqual(out_frame_exp[j], out_frame[j])
+      # TF
+      out_frame = self.evaluate(
+          energy_op.energy(in_frame,
+                           start_index=start_index,
+                           end_index=end_index))
+      for j in range(start_index, end_index):
+        self.assertEqual(out_frame_exp[j], out_frame[j])
+      i += 2
+
+  def testSingleFrame(self):
+    start_index = 5
+    end_index = 250
+    energy_in = [
+        -56, 0, 26, 49, 144, -183, -621, 16, 544, 605, 11, -581, -26, 245,
+        -210, -273, 200, 541, 268, -319, -43, -544, -747, 356, 415, 356, 174,
+        -133, 4, -278, -487, 104, 449, 560, 223, -691, -451, 130, 132, 202, 86,
+        -91, 170, -85, -454, -123, 330, 125, -434, 104, 422, 89, -14, -113,
+        -123, -63, 125, 142, 40, -218, -183, -10, 3, 154, 95, -64, -108, -55,
+        55, 216, 47, -358, -297, 391, 437, 5, -59, -252, -102, -25, -60, 76,
+        -46, 6, 128, 113, -4, -101, 20, -75, -154, 88, 144, -50, -163, 58, 112,
+        38, 31, 2, -38, -80, 77, 63, -136, -83, 83, 89, 32, 27, 6, -237, -247,
+        250, 292, -13, -55, 4, 58, -182, -120, 63, -33, -40, -88, 152, 246, 41,
+        -99, -178, -11, 68, -10, 3, 14, 39, 30, -94, -29, 79, -6, -84, -65, 55,
+        138, 71, -141, -151, 150, 149, -159, -106, 203, 55, -207, -153, -37,
+        231, 187, -6, 54, -66, -85, -258, -244, 271, 157, 24, 117, 144, 144,
+        -202, -66, -320, -478, 340, 510, 46, -152, -185, -199, -19, 139, 282,
+        -15, -140, 129, 45, -124, -26, 145, -36, -79, -17, -85, -29, 104, 82,
+        -84, -7, 127, -96, -210, 60, 114, 67, 40, -3, -1, -101, -76, 77, 55,
+        -5, 19, 13, 13, -36, -40, -34, 20, 63, 7, -66, -44, -6, -22, 66, 40,
+        -20, 13, 21, -15, -45, 6, 38, 19, -40, -46, -3, 2, 41, 41, -17, -37,
+        -11, 15, 13, -4, -5, 0, 1, 2, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 1,
+        1, -1, -2, -1, 0, -1, 0, 0, -1, 0, -1, 0, 0, 1, 0, -1, 0, 1, -1, -1, 0,
+        0, 0, -1, 0, -1, 0, 1, 0, 0, -1, -1, 1, -1, -1, 0, 0, 0, 0, -2, -1, -1,
+        0, 0, -1, -1, 0, 0, -1, -1, -1, 1, 0, -1, 0, 0, 0, -1, 0, 0, 0, 1, -1,
+        -1, 0, 1, 0, -1, -1, 0, -2, 0, 0, 0, 0, -1, -3, 1, 2, 0, 0, 1, 2, -1,
+        -1, -1, -1, -1, -1, 0, 0, 1, 0, -1, -1, 1, 0, 0, 1, -1, 0, 0, 0, 0, 0,
+        -1, 0, -1, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 1, -1, -1, 0, -2, 0, 1,
+        0, 0, 0, 0, 1, -1, -1, 1, 0, -1, 0, 0, -1, 0, 2, 1, -2, -1, 1, 0, 0,
+        -2, 0, 0, -1, -1, 0, 0, 0, 0, -1, -2, -1, 1, 1, 0, 0, 0, 0, -1, -1, 0,
+        1, 1, 0, 0, 0, 0, -1, 0, 0, 0, 0, -1, 0, -1, 0, -1, -1, 0, 0, 0, 1, -1,
+        0, 1, 0, -1, 0, -1, 0, 0, 0, 0, 0, -1, -2, 0, 1, 0, 1, 1, -1, -1, -1,
+        0, 0, 0, 0, -1, -1, -1, 0, 1, 0, -2, -1, 1, 1, 1, -1, -3, -1, 1, -1,
+        -2, 0, -1, -2, -1, 0, 0, 0, -3, -1, 0, 0, -1, 0, 0, -2, 0
+    ]
+    energy_exp = [
+        0, 0, 0, 0, 0, 337682, 60701, 118629, 332681, 173585, 297785, 684745,
+        298961, 47965, 77300, 247985, 515201, 527210, 220301, 58228, 15677,
+        36125, 221245, 124525, 199172, 186005, 12965, 19098, 35789, 49124,
+        33589, 23725, 13121, 14689, 49681, 130373, 241090, 190994, 66985,
+        11029, 9376, 2152, 29153, 10217, 6025, 31460, 23236, 29933, 13988, 965,
+        7844, 9898, 25385, 14810, 1753, 56205, 123509, 85433, 3041, 36488,
+        18369, 2689, 30848, 62197, 41485, 4745, 109, 1717, 9736, 7082, 7092,
+        7250, 24085, 42682, 44701, 36517, 44234, 66258, 54730, 35005, 7272,
+        73789, 132977, 25225, 34425, 61540, 106756, 344084, 262216, 57329,
+        39962, 98845, 19825, 18666, 16052, 22321, 6530, 8066, 17540, 7105,
+        25345, 47700, 17485, 1609, 10202, 11705, 3050, 530, 1465, 2756, 4369,
+        4405, 1972, 4840, 2000, 610, 2250, 1480, 1961, 2125, 1685, 1970, 1490,
+        394, 41, 1, 4, 0, 0, 1, 0, 0, 1, 2, 5, 1, 0, 1, 1, 1, 1, 1, 2, 0, 1, 1,
+        1, 0, 2, 2, 1, 0, 4, 2, 0, 2, 0, 2, 2, 1, 0, 1, 0, 1, 2, 1, 1, 1, 4, 0,
+        1, 10, 4, 1, 5, 2, 2, 1, 1, 1, 2, 0, 2, 0, 0, 1, 1, 0, 2, 0, 0, 2, 1,
+        2, 4, 1, 0, 0, 2, 2, 1, 0, 1, 5, 5, 1, 4, 0, 2, 0, 0, 5, 2, 1, 0, 1, 1,
+        2, 0, 0, 1, 0, 1, 1, 1, 1, 0, 2, 1, 1, 1, 0, 0, 1, 4, 1, 2, 2, 1, 0, 1,
+        2, 1, 4, 2, 2, 10, 2, 5, 1, 0, 0, 0, 0, 0, 0, 0
+    ]
+    energy_out = energy_op.energy(energy_in,
+                                  start_index=start_index,
+                                  end_index=end_index)
+
+    for j in range(start_index, end_index):
+      self.assertEqual(energy_exp[j], energy_out[j])
+
+  def testEnergy(self):
+    self.SingleEnergyTest('testdata/energy_test1.txt')
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/python/tflite_micro/signal/ops/framer_op.py b/python/tflite_micro/signal/ops/framer_op.py
new file mode 100644
index 0000000..f3a471d
--- /dev/null
+++ b/python/tflite_micro/signal/ops/framer_op.py
@@ -0,0 +1,52 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Use framer op in python."""
+
+import tensorflow as tf
+from tflite_micro.python.tflite_micro.signal.utils import util
+
+gen_framer_op = util.load_custom_op('framer_op.so')
+
+
+def _framer_wrapper(framer_fn, default_name):
+  """Wrapper around gen_framer_op.framer*."""
+
+  def _framer(input_tensor,
+              frame_size,
+              frame_step,
+              prefill=False,
+              name=default_name):
+    if frame_step > frame_size:
+      raise ValueError("frame_step must not be greater than frame_size.")
+    with tf.name_scope(name) as name:
+      input_tensor = tf.convert_to_tensor(input_tensor, dtype=tf.int16)
+      dim_list = input_tensor.shape.as_list()
+      if dim_list[-1] % frame_step != 0:
+        raise ValueError(
+            "Innermost input dimenion size must be a multiple of %d elements" %
+            frame_step)
+      return framer_fn(input_tensor,
+                       frame_size=frame_size,
+                       frame_step=frame_step,
+                       prefill=prefill,
+                       name=name)
+
+  return _framer
+
+
+# TODO(b/286250473): change back name after name clash resolved
+framer = _framer_wrapper(gen_framer_op.signal_framer, "signal_framer")
+
+tf.no_gradient("signal_framer")
diff --git a/python/tflite_micro/signal/ops/framer_op_test.py b/python/tflite_micro/signal/ops/framer_op_test.py
new file mode 100644
index 0000000..8f04ed1
--- /dev/null
+++ b/python/tflite_micro/signal/ops/framer_op_test.py
@@ -0,0 +1,142 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for framer op."""
+import os
+
+import numpy as np
+import tensorflow as tf
+
+from tensorflow.python.platform import resource_loader
+from tflite_micro.python.tflite_micro.signal.ops import framer_op
+from tflite_micro.python.tflite_micro.signal.utils import util
+
+
+class FramerOpTest(tf.test.TestCase):
+
+  _PREFIX_PATH = resource_loader.get_path_to_datafile('')
+
+  def GetResource(self, filepath):
+    full_path = os.path.join(self._PREFIX_PATH, filepath)
+    with open(full_path, 'rt') as f:
+      file_text = f.read()
+    return file_text
+
+  def SingleFramerTest(self, filename):
+    lines = self.GetResource(filename).splitlines()
+    args = lines[0].split()
+    frame_size = int(args[0])
+    frame_step = int(args[1])
+    prefill = bool(int(args[2]))
+    func = tf.function(framer_op.framer)
+    input_size = len(lines[1].split())
+    concrete_function = func.get_concrete_function(
+        tf.TensorSpec(input_size, dtype=tf.int16), frame_size, frame_step,
+        prefill)
+    interpreter = util.get_tflm_interpreter(concrete_function, func)
+    # Skip line 0, which contains the configuration params.
+    # Read lines in triplets <input, expected output, expected valid>
+    i = 1
+    while i < len(lines):
+      in_block = np.array([int(j) for j in lines[i].split()], dtype=np.int16)
+      out_frame_exp = [[int(j) for j in lines[i + 1].split()]]
+      out_valid_exp = [int(j) for j in lines[i + 2].split()]
+      # TFLM
+      interpreter.set_input(in_block, 0)
+      interpreter.invoke()
+      out_frame = interpreter.get_output(0)
+      out_valid = interpreter.get_output(1)
+      self.assertEqual(out_valid, out_valid_exp)
+      if out_valid:
+        self.assertAllEqual(out_frame, out_frame_exp)
+      # TF
+      out_frame, out_valid = self.evaluate(
+          framer_op.framer(in_block, frame_size, frame_step, prefill))
+      self.assertEqual(out_valid, out_valid_exp)
+      if out_valid:
+        self.assertAllEqual(out_frame, out_frame_exp)
+      i += 3
+
+  def MultiFrameRandomInputFramerTest(self, n_frames):
+    # Terminonlogy: input is in blocks, output is in frames
+    frame_step = 160
+    frame_size = 400
+    prefill = True
+    block_num = 10
+    block_size = frame_step * n_frames
+
+    test_input = np.random.randint(np.iinfo('int16').min,
+                                   np.iinfo('int16').max,
+                                   block_size * block_num,
+                                   dtype=np.int16)
+    expected_output = np.concatenate((np.zeros(frame_size - frame_step,
+                                               dtype=np.int16), test_input))
+    func = tf.function(framer_op.framer)
+    concrete_function = func.get_concrete_function(
+        tf.TensorSpec(block_size, dtype=tf.int16), frame_size, frame_step,
+        prefill)
+    interpreter = util.get_tflm_interpreter(concrete_function, func)
+    block_index = 0
+    frame_index = 0
+    while block_index < block_num:
+      in_block = test_input[(block_index * block_size):((block_index + 1) *
+                                                        block_size)]
+      expected_valid = 1
+      expected_frame = [
+          expected_output[((frame_index + i) *
+                           frame_step):((frame_index + i) * frame_step +
+                                        frame_size)] for i in range(n_frames)
+      ]
+      # TFLM
+      interpreter.set_input(in_block, 0)
+      interpreter.invoke()
+      out_frame = interpreter.get_output(0)
+      out_valid = interpreter.get_output(1)
+      self.assertEqual(out_valid, expected_valid)
+      if out_valid:
+        self.assertAllEqual(out_frame, expected_frame)
+      # TF
+      out_frame, out_valid = self.evaluate(
+          framer_op.framer(in_block, frame_size, frame_step, prefill))
+      frame_index += n_frames
+      self.assertEqual(out_valid, expected_valid)
+      self.assertAllEqual(out_frame, expected_frame)
+      block_index += 1
+
+  def testFramerVectors(self):
+    self.SingleFramerTest('testdata/framer_test1.txt')
+
+  def testFramerRandomInput(self):
+    self.MultiFrameRandomInputFramerTest(1)
+
+  def testFramerRandomInputNframes2(self):
+    self.MultiFrameRandomInputFramerTest(2)
+
+  def testFramerRandomInputNframes4(self):
+    self.MultiFrameRandomInputFramerTest(4)
+
+  def testStepSizeTooLarge(self):
+    framer_input = np.zeros(160, dtype=np.int16)
+    with self.assertRaises((tf.errors.InvalidArgumentError, ValueError)):
+      self.evaluate(framer_op.framer(framer_input, 128, 129))
+
+  def testStepSizeNotEqualInputSize(self):
+    framer_input = np.zeros(122, dtype=np.int16)
+    with self.assertRaises((tf.errors.InvalidArgumentError, ValueError)):
+      self.evaluate(framer_op.framer(framer_input, 321, 123))
+
+
+if __name__ == '__main__':
+  np.random.seed(0)
+  tf.test.main()
diff --git a/python/tflite_micro/signal/ops/overlap_add_op.py b/python/tflite_micro/signal/ops/overlap_add_op.py
new file mode 100644
index 0000000..d6e7080
--- /dev/null
+++ b/python/tflite_micro/signal/ops/overlap_add_op.py
@@ -0,0 +1,42 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Use overlap add op in python."""
+
+import tensorflow as tf
+from tflite_micro.python.tflite_micro.signal.utils import util
+
+gen_overlap_add_op = util.load_custom_op('overlap_add_op.so')
+
+
+def _overlap_add_wrapper(overlap_add_fn, default_name):
+  """Wrapper around gen_overlap_add_op.overlap_add*."""
+
+  def _overlap_add(input_tensor, frame_step, name=default_name):
+    with tf.name_scope(name) as name:
+      input_tensor = tf.convert_to_tensor(input_tensor)
+      dim_list = input_tensor.shape.as_list()
+      if frame_step > dim_list[-1]:
+        raise ValueError(
+            "Frame_step must not exceed innermost input dimension")
+      return overlap_add_fn(input_tensor, frame_step=frame_step, name=name)
+
+  return _overlap_add
+
+
+# TODO(b/286250473): change back name after name clash resolved
+overlap_add = _overlap_add_wrapper(gen_overlap_add_op.signal_overlap_add,
+                                   "signal_overlap_add")
+
+tf.no_gradient("signal_overlap_add")
diff --git a/python/tflite_micro/signal/ops/overlap_add_op_test.py b/python/tflite_micro/signal/ops/overlap_add_op_test.py
new file mode 100644
index 0000000..ffd2ae3
--- /dev/null
+++ b/python/tflite_micro/signal/ops/overlap_add_op_test.py
@@ -0,0 +1,232 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for overlap add op."""
+
+from absl.testing import parameterized
+import numpy as np
+import tensorflow as tf
+
+from tflite_micro.python.tflite_micro.signal.ops import overlap_add_op
+from tflite_micro.python.tflite_micro.signal.utils import util
+
+
+class OverlapAddOpTest(parameterized.TestCase, tf.test.TestCase):
+
+  def RunOverlapAdd(self, interpreter, input_frames, frame_step,
+                    expected_output_frames, dtype):
+    input_frames = tf.convert_to_tensor(input_frames, dtype=dtype)
+    # TFLM
+    interpreter.set_input(input_frames, 0)
+    interpreter.invoke()
+    output_frame = interpreter.get_output(0)
+    self.assertAllEqual(output_frame, expected_output_frames)
+
+    # TF
+    output_frame = self.evaluate(
+        overlap_add_op.overlap_add(input_frames, frame_step))
+    self.assertAllEqual(output_frame, expected_output_frames)
+
+  @parameterized.named_parameters(('_FLOAT32InputOutput', tf.float32),
+                                  ('_INT16InputOutput', tf.int16))
+  def testOverlapAddValidInput(self, dtype):
+    input_frames = np.array([[1, -5, 4, 2, 7], [4, 15, -44, 27, -16],
+                             [66, -19, 79, 8, -12], [-122, 17, 65, 18, -101],
+                             [3, 33, -66, -19, 55]])
+    expected_output_frames_step_1 = np.array([[1], [-1], [85], [-183], [133]])
+    expected_output_frames_step_2 = np.array([[1, -5], [8, 17], [29, 8],
+                                              [-59, 25], [56, 51]])
+    expected_output_frames_step_3 = np.array([[1, -5, 4], [6, 22, -44],
+                                              [93, -35, 79], [-114, 5, 65],
+                                              [21, -68, -66]])
+    expected_output_frames_step_4 = np.array([[1, -5, 4, 2], [11, 15, -44, 27],
+                                              [50, -19, 79, 8],
+                                              [-134, 17, 65, 18],
+                                              [-98, 33, -66, -19]])
+    expected_output_frames_step_5 = np.array([[1, -5, 4, 2, 7],
+                                              [4, 15, -44, 27, -16],
+                                              [66, -19, 79, 8, -12],
+                                              [-122, 17, 65, 18, -101],
+                                              [3, 33, -66, -19, 55]])
+    func = tf.function(overlap_add_op.overlap_add)
+    # Initialize an interpreter for each step size
+    # TODO(b/263020764): use a parameterized test instead
+    interpreters = [None] * 6
+    for i in range(5):
+      interpreters[i] = util.get_tflm_interpreter(
+          func.get_concrete_function(
+              tf.TensorSpec(np.shape([input_frames[0]]), dtype=dtype), i + 1),
+          func)
+
+    frame_num = input_frames.shape[0]
+    frame_index = 0
+    while frame_index < frame_num:
+      self.RunOverlapAdd(interpreters[0], [input_frames[frame_index]],
+                         1,
+                         expected_output_frames_step_1[frame_index],
+                         dtype=dtype)
+      self.RunOverlapAdd(interpreters[1], [input_frames[frame_index]],
+                         2,
+                         expected_output_frames_step_2[frame_index],
+                         dtype=dtype)
+      self.RunOverlapAdd(interpreters[2], [input_frames[frame_index]],
+                         3,
+                         expected_output_frames_step_3[frame_index],
+                         dtype=dtype)
+      self.RunOverlapAdd(interpreters[3], [input_frames[frame_index]],
+                         4,
+                         expected_output_frames_step_4[frame_index],
+                         dtype=dtype)
+      self.RunOverlapAdd(interpreters[4], [input_frames[frame_index]],
+                         5,
+                         expected_output_frames_step_5[frame_index],
+                         dtype=dtype)
+      frame_index += 1
+
+  @parameterized.named_parameters(('_FLOAT32InputOutput', tf.float32),
+                                  ('_INT16InputOutput', tf.int16))
+  def testOverlapAddNframes5(self, dtype):
+    input_frames = np.array([[1, -5, 4, 2, 7], [4, 15, -44, 27, -16],
+                             [66, -19, 79, 8, -12], [-122, 17, 65, 18, -101],
+                             [3, 33, -66, -19, 55]])
+    expected_output_frames_step_1 = np.array([1, -1, 85, -183, 133])
+    expected_output_frames_step_2 = np.array(
+        [1, -5, 8, 17, 29, 8, -59, 25, 56, 51])
+    expected_output_frames_step_3 = np.array(
+        [1, -5, 4, 6, 22, -44, 93, -35, 79, -114, 5, 65, 21, -68, -66])
+    expected_output_frames_step_4 = np.array([
+        1, -5, 4, 2, 11, 15, -44, 27, 50, -19, 79, 8, -134, 17, 65, 18, -98,
+        33, -66, -19
+    ])
+    expected_output_frames_step_5 = np.array([
+        1, -5, 4, 2, 7, 4, 15, -44, 27, -16, 66, -19, 79, 8, -12, -122, 17, 65,
+        18, -101, 3, 33, -66, -19, 55
+    ])
+    func = tf.function(overlap_add_op.overlap_add)
+    # Initialize an interpreter for each step size
+    # TODO(b/263020764): use a parameterized test instead
+    interpreters = [None] * 6
+    for i in range(5):
+      interpreters[i] = util.get_tflm_interpreter(
+          func.get_concrete_function(
+              tf.TensorSpec(np.shape(input_frames), dtype=dtype), i + 1), func)
+    self.RunOverlapAdd(interpreters[0],
+                       input_frames,
+                       1,
+                       expected_output_frames_step_1,
+                       dtype=dtype)
+    self.RunOverlapAdd(interpreters[1],
+                       input_frames,
+                       2,
+                       expected_output_frames_step_2,
+                       dtype=dtype)
+    self.RunOverlapAdd(interpreters[2],
+                       input_frames,
+                       3,
+                       expected_output_frames_step_3,
+                       dtype=dtype)
+    self.RunOverlapAdd(interpreters[3],
+                       input_frames,
+                       4,
+                       expected_output_frames_step_4,
+                       dtype=dtype)
+    self.RunOverlapAdd(interpreters[4],
+                       input_frames,
+                       5,
+                       expected_output_frames_step_5,
+                       dtype=dtype)
+
+  @parameterized.named_parameters(('_FLOAT32InputOutput', tf.float32),
+                                  ('_INT16InputOutput', tf.int16))
+  def testOverlapAddNframes5Channels2(self, dtype):
+    input_frames = np.array([[[1, -5, 4, 2, 7], [4, 15, -44, 27, -16],
+                              [66, -19, 79, 8, -12], [-122, 17, 65, 18, -101],
+                              [3, 33, -66, -19, 55]],
+                             [[1, -5, 4, 2, 7], [4, 15, -44, 27, -16],
+                              [66, -19, 79, 8, -12], [-122, 17, 65, 18, -101],
+                              [3, 33, -66, -19, 55]]])
+    expected_output_frames_step_1 = np.array([[1, -1, 85, -183, 133],
+                                              [1, -1, 85, -183, 133]])
+    expected_output_frames_step_2 = np.array(
+        [[1, -5, 8, 17, 29, 8, -59, 25, 56, 51],
+         [1, -5, 8, 17, 29, 8, -59, 25, 56, 51]])
+    expected_output_frames_step_3 = np.array(
+        [[1, -5, 4, 6, 22, -44, 93, -35, 79, -114, 5, 65, 21, -68, -66],
+         [1, -5, 4, 6, 22, -44, 93, -35, 79, -114, 5, 65, 21, -68, -66]])
+    expected_output_frames_step_4 = np.array([[
+        1, -5, 4, 2, 11, 15, -44, 27, 50, -19, 79, 8, -134, 17, 65, 18, -98,
+        33, -66, -19
+    ],
+                                              [
+                                                  1, -5, 4, 2, 11, 15, -44, 27,
+                                                  50, -19, 79, 8, -134, 17, 65,
+                                                  18, -98, 33, -66, -19
+                                              ]])
+    expected_output_frames_step_5 = np.array([[
+        1, -5, 4, 2, 7, 4, 15, -44, 27, -16, 66, -19, 79, 8, -12, -122, 17, 65,
+        18, -101, 3, 33, -66, -19, 55
+    ],
+                                              [
+                                                  1, -5, 4, 2, 7, 4, 15, -44,
+                                                  27, -16, 66, -19, 79, 8, -12,
+                                                  -122, 17, 65, 18, -101, 3,
+                                                  33, -66, -19, 55
+                                              ]])
+    func = tf.function(overlap_add_op.overlap_add)
+    # Initialize an interpreter for each step size
+    # TODO(b/263020764): use a parameterized test instead
+    interpreters = [None] * 6
+    for i in range(5):
+      interpreters[i] = util.get_tflm_interpreter(
+          func.get_concrete_function(
+              tf.TensorSpec(np.shape(input_frames), dtype=dtype), i + 1), func)
+    self.RunOverlapAdd(interpreters[0],
+                       input_frames,
+                       1,
+                       expected_output_frames_step_1,
+                       dtype=dtype)
+    self.RunOverlapAdd(interpreters[1],
+                       input_frames,
+                       2,
+                       expected_output_frames_step_2,
+                       dtype=dtype)
+    self.RunOverlapAdd(interpreters[2],
+                       input_frames,
+                       3,
+                       expected_output_frames_step_3,
+                       dtype=dtype)
+    self.RunOverlapAdd(interpreters[3],
+                       input_frames,
+                       4,
+                       expected_output_frames_step_4,
+                       dtype=dtype)
+    self.RunOverlapAdd(interpreters[4],
+                       input_frames,
+                       5,
+                       expected_output_frames_step_5,
+                       dtype=dtype)
+
+  def testStepSizeTooLarge(self):
+    ovlerap_add_input = np.zeros(160, dtype=np.int16)
+    with self.assertRaises((tf.errors.InvalidArgumentError, ValueError)):
+      self.evaluate(overlap_add_op.overlap_add(ovlerap_add_input, 128, 129))
+
+  def testStepSizeNotEqualOutputSize(self):
+    ovlerap_add_input = np.zeros(122, dtype=np.int16)
+    with self.assertRaises((tf.errors.InvalidArgumentError, ValueError)):
+      self.evaluate(overlap_add_op.overlap_add(ovlerap_add_input, 321, 123))
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/python/tflite_micro/signal/ops/testdata/BUILD b/python/tflite_micro/signal/ops/testdata/BUILD
index ea8d313..beb8123 100644
--- a/python/tflite_micro/signal/ops/testdata/BUILD
+++ b/python/tflite_micro/signal/ops/testdata/BUILD
@@ -7,7 +7,9 @@
 )
 
 exports_files([
+    "energy_test1.txt",
     "fft_auto_scale_test1.txt",
+    "framer_test1.txt",
     "rfft_test1.txt",
     "window_test1.txt",
 ])
diff --git a/python/tflite_micro/signal/ops/testdata/energy_test1.txt b/python/tflite_micro/signal/ops/testdata/energy_test1.txt
new file mode 100644
index 0000000..e1a972c
--- /dev/null
+++ b/python/tflite_micro/signal/ops/testdata/energy_test1.txt
@@ -0,0 +1,367 @@
+5 122
+-65 0 5 4 -86 148 379 -160 -630 -280 337 1003 324 -1169 -646 680 626 -208 -683 27 663 159 -235 -401 -402 421 617 62 52 -679 -1095 567 1255 284 -387 -687 -263 275 163 80 42 -9 -53 -72 34 107 78 -185 -219 106 101 70 128 -50 -130 -48 -10 -9 51 129 -1 -149 -55 98 94 -17 -47 -83 -100 97 158 20 -33 -54 -41 -62 -69 110 166 -21 -121 33 23 -142 196 182 -463 -240 426 264 -177 22 205 -364 -358 257 214 17 -7 -30 -8 -12 35 -63 -156 59 157 69 -17 -129 -108 103 138 -63 -68 74 39 -120 -82 51 31 79 78 -39 -38 -77 -36 59 2 -6 45 3 -66 35 140 -68 -180 7 115 32 -56 -3 38 -5 -6 -1 -20 -26 27 51 -42 -67 54 86 -36 -100 -11 57 3 37 72 -59 -77 -21 -13 24 15 67 52 -73 -12 30 -37 -84 -29 85 32 48 64 -97 -65 45 12 -39 -26 14 -11 82 131 -71 -143 -65 0 127 122 -30 -109 -64 78 78 -81 -107 39 202 88 -232 -136 142 78 -104 -72 132 115 -68 -28 -85 -92 189 92 -337 -42 578 11 -728 140 775 -249 -885 212 906 -147 -833 107 687 -89 -569 11 424 28 -238 -12 78 -38 5 40 -28 -26 35 20 -26 -16 4 1 5 5 0 0 1 -1 -4 3 -2 -6 2 3 -1 2 -3 -8 2 8 0 -4 -7 -3 3 -2 3 -2 -3 3 4 -3 -6 -3 5 -1 -2 -1 4 2 -2 -4 0 -3 -2 0 1 -5 0 -1 -1 0 4 -1 2 0 0 -4 -1 1 5 3 0 -5 0 3 -2 -1 0 -2 3 2 1 0 -1 0 0 2 -1 -1 -1 -3 -3 -3 4 3 -1 -6 0 3 3 -5 0 0 6 1 -2 2 4 1 -4 -1 -2 -4 -1 3 3 -3 1 2 0 -3 -1 2 2 0 3 1 -4 1 1 -3 -4 6 7 -3 -2 2 -4 5 -1 -1 -2 -2 -4 -2 -4 -1 5 0 1 0 -3 1 0 0 -3 -5 2 2 -1 1 -2 -2 0 -2 0 -1 -2 -1 3 3 -1 -3 1 2 -2 -2 1 -1 0 -2 -1 2 1 -2 -1 1 3 2 -1 -3 1 0 -4 2 1 -5 2 2 1 4 0 -5 2 2 -1 0 3 4 -1 2 -2 0 -3 -2 2 2 2 2 -2 -3 -3 3 4 0 -4 -4 -1 1 -3 -1 -1 -4 0 -1 1 2 1 -2 1 2 -1 -3 3 3 0 -1 -3 4 4 -3 -7 -4 3 3 2 2 -2 -2 -2 -1 -1 0 0 0 -3 -4 2 1 0 1 0
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diff --git a/python/tflite_micro/signal/ops/testdata/framer_test1.txt b/python/tflite_micro/signal/ops/testdata/framer_test1.txt
new file mode 100644
index 0000000..3c81462
--- /dev/null
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+1
\ No newline at end of file
diff --git a/signal/tensorflow_core/kernels/BUILD b/signal/tensorflow_core/kernels/BUILD
index 7d412eb..a626b5d 100644
--- a/signal/tensorflow_core/kernels/BUILD
+++ b/signal/tensorflow_core/kernels/BUILD
@@ -15,6 +15,16 @@
 )
 
 tflm_signal_kernel_library(
+    name = "energy_kernel",
+    srcs = ["energy_kernel.cc"],
+    deps = [
+        "//signal/src:complex",
+        "//signal/src:energy",
+        "@tensorflow_cc_deps//:cc_library",
+    ],
+)
+
+tflm_signal_kernel_library(
     name = "fft_kernel",
     srcs = ["fft_kernels.cc"],
     deps = [
@@ -26,6 +36,24 @@
 )
 
 tflm_signal_kernel_library(
+    name = "framer_kernel",
+    srcs = ["framer_kernel.cc"],
+    deps = [
+        "//signal/src:circular_buffer",
+        "@tensorflow_cc_deps//:cc_library",
+    ],
+)
+
+tflm_signal_kernel_library(
+    name = "overlap_add_kernel",
+    srcs = ["overlap_add_kernel.cc"],
+    deps = [
+        "//signal/src:overlap_add",
+        "@tensorflow_cc_deps//:cc_library",
+    ],
+)
+
+tflm_signal_kernel_library(
     name = "window_kernel",
     srcs = ["window_kernel.cc"],
     deps = [
diff --git a/signal/tensorflow_core/kernels/energy_kernel.cc b/signal/tensorflow_core/kernels/energy_kernel.cc
new file mode 100644
index 0000000..11fda5e
--- /dev/null
+++ b/signal/tensorflow_core/kernels/energy_kernel.cc
@@ -0,0 +1,56 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "signal/src/complex.h"
+#include "signal/src/energy.h"
+#include "tensorflow/core/framework/op_kernel.h"
+
+namespace tensorflow {
+namespace signal {
+
+class EnergyOp : public tensorflow::OpKernel {
+ public:
+  explicit EnergyOp(tensorflow::OpKernelConstruction* context)
+      : tensorflow::OpKernel(context) {
+    OP_REQUIRES_OK(context, context->GetAttr("start_index", &start_index_));
+    OP_REQUIRES_OK(context, context->GetAttr("end_index", &end_index_));
+  }
+  void Compute(tensorflow::OpKernelContext* context) override {
+    const tensorflow::Tensor& input_tensor = context->input(0);
+    const int16_t* input = input_tensor.flat<int16_t>().data();
+    tensorflow::Tensor* output_tensor = nullptr;
+    // The input is complex. The output is real.
+    int output_size = input_tensor.flat<int16>().size() >> 1;
+
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(0, {output_size}, &output_tensor));
+    uint32* output = output_tensor->flat<uint32>().data();
+
+    tflite::tflm_signal::SpectrumToEnergy(
+        reinterpret_cast<const Complex<int16_t>*>(input), start_index_,
+        end_index_, output);
+  }
+
+ private:
+  int start_index_;
+  int end_index_;
+};
+
+// TODO(b/286250473): change back name after name clash resolved
+REGISTER_KERNEL_BUILDER(Name("SignalEnergy").Device(tensorflow::DEVICE_CPU),
+                        EnergyOp);
+
+}  // namespace signal
+}  // namespace tensorflow
diff --git a/signal/tensorflow_core/kernels/framer_kernel.cc b/signal/tensorflow_core/kernels/framer_kernel.cc
new file mode 100644
index 0000000..e858de3
--- /dev/null
+++ b/signal/tensorflow_core/kernels/framer_kernel.cc
@@ -0,0 +1,126 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <cstdint>
+
+#include "signal/src/circular_buffer.h"
+#include "tensorflow/core/framework/op_kernel.h"
+
+namespace tensorflow {
+namespace signal {
+
+class FramerOp : public tensorflow::OpKernel {
+ public:
+  explicit FramerOp(tensorflow::OpKernelConstruction* context)
+      : tensorflow::OpKernel(context) {
+    OP_REQUIRES_OK(context, context->GetAttr("frame_size", &frame_size_));
+    OP_REQUIRES_OK(context, context->GetAttr("frame_step", &frame_step_));
+    OP_REQUIRES_OK(context, context->GetAttr("prefill", &prefill_));
+    initialized_ = false;
+  }
+
+  ~FramerOp() {}
+
+  void Compute(tensorflow::OpKernelContext* context) override {
+    const tensorflow::Tensor& input_tensor = context->input(0);
+    if (!initialized_) {
+      n_frames_ = input_tensor.flat_inner_dims<int16_t>().dimensions().at(1) /
+                  frame_step_;
+      outer_dims_ = input_tensor.flat_inner_dims<int16_t>().dimensions().at(0);
+
+      state_tensors_.resize(outer_dims_);
+      circular_buffers_.resize(outer_dims_);
+
+      // Calculate the capacity of the circular buffer. Round up the frame size
+      // to
+      // a multiple of frame step. Saves memory relative to the simpler
+      // frame_size + frame_step. For example:
+      // step_size = 160, frame_size = 400
+      // capacity = 480 vs. step_size + frame_size = 560
+      size_t capacity =
+          (frame_size_ + frame_step_ - 1) / frame_step_ * frame_step_;
+      size_t state_size =
+          tflite::tflm_signal::CircularBufferGetNeededMemory(capacity);
+      for (int i = 0; i < outer_dims_; i++) {
+        OP_REQUIRES_OK(
+            context,
+            context->allocate_temp(
+                DT_INT8, TensorShape({static_cast<int32_t>(state_size)}),
+                &state_tensors_[i]));
+        int8_t* state_ = state_tensors_[i].flat<int8_t>().data();
+        circular_buffers_[i] = tflite::tflm_signal::CircularBufferInit(
+            capacity, state_, state_size);
+        if (prefill_) {
+          tflite::tflm_signal::CircularBufferWriteZeros(
+              circular_buffers_[i], frame_size_ - frame_step_);
+        }
+      }
+
+      initialized_ = true;
+    }
+
+    // Split the last dimension of the input into {n_frames_, frame_size_}.
+    TensorShape output_shape = input_tensor.shape();
+    output_shape.AddDim(frame_size_);
+    output_shape.set_dim(output_shape.dims() - 2, n_frames_);
+
+    tensorflow::Tensor* output_tensor = nullptr;
+    tensorflow::Tensor* output_valid_tensor = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(0, output_shape, &output_tensor));
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(1, {}, &output_valid_tensor));
+
+    bool output_valid = true;
+    for (int i = 0; i < outer_dims_; i++) {
+      for (int frame = 0; frame < n_frames_; frame++) {
+        int input_index = (i * n_frames_ + frame) * frame_step_;
+        int output_index = (i * n_frames_ + frame) * frame_size_;
+        tflite::tflm_signal::CircularBufferWrite(
+            circular_buffers_[i],
+            &(input_tensor.flat<int16_t>().data())[input_index], frame_step_);
+        if (tflite::tflm_signal::CircularBufferAvailable(
+                circular_buffers_[i]) >= (unsigned)frame_size_) {
+          tflite::tflm_signal::CircularBufferGet(
+              circular_buffers_[i], frame_size_,
+              &(reinterpret_cast<int16_t*>(
+                  output_tensor->data()))[output_index]);
+          tflite::tflm_signal::CircularBufferDiscard(circular_buffers_[i],
+                                                     frame_step_);
+        } else {
+          output_valid = false;
+        }
+      }
+      *output_valid_tensor->flat<bool>().data() = output_valid;
+    }
+  }
+
+ private:
+  bool initialized_;
+  int frame_size_;
+  int frame_step_;
+  int outer_dims_;
+  bool prefill_;
+  int n_frames_;
+  std::vector<Tensor> state_tensors_;
+  std::vector<struct tflite::tflm_signal::CircularBuffer*> circular_buffers_;
+};
+
+// TODO(b/286250473): change back name after name clash resolved
+REGISTER_KERNEL_BUILDER(Name("SignalFramer").Device(tensorflow::DEVICE_CPU),
+                        FramerOp);
+
+}  // namespace signal
+}  // namespace tensorflow
diff --git a/signal/tensorflow_core/kernels/overlap_add_kernel.cc b/signal/tensorflow_core/kernels/overlap_add_kernel.cc
new file mode 100644
index 0000000..40dbad8
--- /dev/null
+++ b/signal/tensorflow_core/kernels/overlap_add_kernel.cc
@@ -0,0 +1,91 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <cstdint>
+
+#include "signal/src/overlap_add.h"
+#include "tensorflow/core/framework/op_kernel.h"
+
+namespace tensorflow {
+namespace signal {
+
+template <typename T, DataType E>
+class OverlapAddOp : public tensorflow::OpKernel {
+ public:
+  explicit OverlapAddOp(tensorflow::OpKernelConstruction* context)
+      : tensorflow::OpKernel(context) {
+    OP_REQUIRES_OK(context, context->GetAttr("frame_step", &frame_step_));
+    initialized_ = false;
+  }
+
+  void Compute(tensorflow::OpKernelContext* context) override {
+    const tensorflow::Tensor& input_tensor = context->input(0);
+    if (!initialized_) {
+      outer_dims_ = input_tensor.flat_inner_dims<T, 3>().dimensions().at(0);
+      n_frames_ = input_tensor.flat_inner_dims<T, 3>().dimensions().at(1);
+      frame_size_ = input_tensor.flat_inner_dims<T, 3>().dimensions().at(2);
+
+      state_tensors_.resize(outer_dims_);
+      for (int i = 0; i < outer_dims_; i++) {
+        OP_REQUIRES_OK(context,
+                       context->allocate_temp(
+                           E, TensorShape({static_cast<int32_t>(frame_size_)}),
+                           &state_tensors_[i]));
+        memset(state_tensors_[i].flat<T>().data(), 0, sizeof(T) * frame_size_);
+      }
+      initialized_ = true;
+    }
+
+    TensorShape output_shape = input_tensor.shape();
+    output_shape.RemoveDim(output_shape.dims() - 1);
+    output_shape.set_dim(output_shape.dims() - 1, n_frames_ * frame_step_);
+
+    tensorflow::Tensor* output_tensor = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(0, output_shape, &output_tensor));
+    const T* input = input_tensor.flat<T>().data();
+    T* output = output_tensor->flat<T>().data();
+    for (int i = 0; i < outer_dims_; i++) {
+      T* buffer = state_tensors_[i].flat<T>().data();
+      for (int frame = 0; frame < n_frames_; frame++) {
+        int input_index = (i * n_frames_ + frame) * frame_size_;
+        int output_index = (i * n_frames_ + frame) * frame_step_;
+        tflm_signal::OverlapAdd(&input[input_index], buffer, frame_size_,
+                                &output[output_index], frame_step_);
+      }
+    }
+  }
+
+ private:
+  int frame_size_;
+  int frame_step_;
+  int n_frames_;
+  int outer_dims_;
+  bool initialized_;
+  std::vector<Tensor> state_tensors_;
+};
+
+// TODO(b/286250473): change back name after name clash resolved
+REGISTER_KERNEL_BUILDER(Name("SignalOverlapAdd")
+                            .Device(tensorflow::DEVICE_CPU)
+                            .TypeConstraint<float>("T"),
+                        OverlapAddOp<float, DT_FLOAT>);
+REGISTER_KERNEL_BUILDER(Name("SignalOverlapAdd")
+                            .Device(tensorflow::DEVICE_CPU)
+                            .TypeConstraint<int16_t>("T"),
+                        OverlapAddOp<int16_t, DT_INT16>);
+
+}  // namespace signal
+}  // namespace tensorflow
diff --git a/signal/tensorflow_core/ops/BUILD b/signal/tensorflow_core/ops/BUILD
index 436dde5..2c5803b 100644
--- a/signal/tensorflow_core/ops/BUILD
+++ b/signal/tensorflow_core/ops/BUILD
@@ -14,6 +14,14 @@
 )
 
 tflm_signal_kernel_library(
+    name = "energy_op",
+    srcs = ["energy_op.cc"],
+    deps = [
+        "@tensorflow_cc_deps//:cc_library",
+    ],
+)
+
+tflm_signal_kernel_library(
     name = "fft_ops",
     srcs = ["fft_ops.cc"],
     deps = [
@@ -22,6 +30,22 @@
 )
 
 tflm_signal_kernel_library(
+    name = "framer_op",
+    srcs = ["framer_op.cc"],
+    deps = [
+        "@tensorflow_cc_deps//:cc_library",
+    ],
+)
+
+tflm_signal_kernel_library(
+    name = "overlap_add_op",
+    srcs = ["overlap_add_op.cc"],
+    deps = [
+        "@tensorflow_cc_deps//:cc_library",
+    ],
+)
+
+tflm_signal_kernel_library(
     name = "window_op",
     srcs = ["window_op.cc"],
     deps = [
diff --git a/signal/tensorflow_core/ops/energy_op.cc b/signal/tensorflow_core/ops/energy_op.cc
new file mode 100644
index 0000000..9ca74ed
--- /dev/null
+++ b/signal/tensorflow_core/ops/energy_op.cc
@@ -0,0 +1,56 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+using tensorflow::shape_inference::InferenceContext;
+using tensorflow::shape_inference::ShapeHandle;
+
+namespace tensorflow {
+namespace signal {
+
+Status EnergyShape(InferenceContext* c) {
+  ShapeHandle out;
+
+  TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &out));
+  int64_t length = InferenceContext::Value(c->Dim(out, 0)) / 2;
+
+  TF_RETURN_IF_ERROR(c->ReplaceDim(out, 0, c->MakeDim(length), &out));
+  c->set_output(0, out);
+  return OkStatus();
+}
+
+// TODO(b/286250473): change back name after name clash resolved
+REGISTER_OP("SignalEnergy")
+    .Attr("start_index: int")
+    .Attr("end_index: int")
+    .Input("input: int16")
+    .Output("output: uint32")
+    .SetShapeFn([](InferenceContext* c) { return EnergyShape(c); })
+    .Doc(R"doc(
+Calculate the energy of a spectral array. Only elements in the index range
+[start_index, end_index] are calculated, and the rest are set to zero.
+
+input: A 1-D frequency domain signal of complex int16 elements
+output: A 1-D array of uint32 elements containing the square of the absolute
+        value of each element in input.
+
+start_index: index in input to start calculating from. Default: 0
+end_index: last index in the input to calculate. Default: last element of input
+)doc");
+
+}  // namespace signal
+}  // namespace tensorflow
diff --git a/signal/tensorflow_core/ops/framer_op.cc b/signal/tensorflow_core/ops/framer_op.cc
new file mode 100644
index 0000000..ebac6e7
--- /dev/null
+++ b/signal/tensorflow_core/ops/framer_op.cc
@@ -0,0 +1,91 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+using tensorflow::shape_inference::InferenceContext;
+using tensorflow::shape_inference::ShapeHandle;
+
+namespace tensorflow {
+namespace signal {
+
+Status FramerShape(InferenceContext* c) {
+  ShapeHandle unused;
+  ShapeHandle in;
+  int frame_step, frame_size;
+
+  TF_RETURN_IF_ERROR(c->GetAttr<int>("frame_step", &frame_step));
+  TF_RETURN_IF_ERROR(c->GetAttr<int>("frame_size", &frame_size));
+  TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &in));
+  int n_frames = c->Value(c->Dim(in, -1)) / frame_step;
+
+  shape_inference::DimensionHandle extra_dim = c->MakeDim({frame_size});
+  ShapeHandle extra_dim_shape =
+      c->MakeShape(std::vector<shape_inference::DimensionHandle>({extra_dim}));
+  ShapeHandle out;
+  TF_RETURN_IF_ERROR(c->Concatenate(in, extra_dim_shape, &out));
+  TF_RETURN_IF_ERROR(c->ReplaceDim(out, -2, c->MakeDim(n_frames), &out));
+  TF_RETURN_IF_ERROR(c->ReplaceDim(out, -1, c->MakeDim(frame_size), &out));
+  c->set_output(0, out);
+  c->set_output(1, c->Scalar());
+  return OkStatus();
+}
+
+// TODO(b/286250473): change back name after name clash resolved
+REGISTER_OP("SignalFramer")
+    .Attr("frame_size: int >= 1")
+    .Attr("frame_step: int >= 1")
+    .Attr("prefill: bool")
+    .Input("input: int16")
+    .Output("output: int16")
+    .Output("output_valid: bool")
+    .SetShapeFn([](InferenceContext* c) { return FramerShape(c); })
+    .Doc(R"doc(
+Transform an input signal into a series of overlapping frames, each of
+size frame_size. The frame_step determines how many samples the framer
+progresses on each invocation. When the framer has enough samples to produce
+a frame, it writes it to the output tensor and sets the output_valid to True.
+If the framer doesn't have enough samples to produce a frame, it doesn't
+initialize the contents of the output tensor and sets the output_valid boolean
+to False.
+For example, assuming an input signal of 10 samples,
+[1 2 3 4 5 6 7 8 9 0]
+and the framer is invoked 5 times, each time with two samples.
+For a frame with the configuration:
+frame_size=3
+frame_step=2
+The framer will produce the following output:
+input: [1, 2], output: [undefined, undefined, undefined], output_valid: False
+input: [3, 4], output: [1, 2, 3], output_valid: True
+input: [5, 6], output: [3, 4, 5], output_valid: True
+input: [7, 8], output: [5, 6, 7], output_valid: True
+input: [9, 0], output: [7, 6, 8], output_valid: True
+
+input: A 1-D input signal frame. Size must be a multiple of frame_step
+output: A 2-D output frame with innermost dimension frame_size
+output_valid: A boolean scalar.
+If True, the output is a valid output frame
+If False, the output is an invalid output frame of all zeros
+
+frame_size: The number of samples in each output frame
+frame_step: The number of input samples to progress the framer's 'current'
+sample on each invocation.
+prefill: If true, initialize the framer with (frame_size - frame_step) zeros.
+Can be used to guarantee a valid output starting with the first input.
+)doc");
+
+}  // namespace signal
+}  // namespace tensorflow
diff --git a/signal/tensorflow_core/ops/overlap_add_op.cc b/signal/tensorflow_core/ops/overlap_add_op.cc
new file mode 100644
index 0000000..759faa9
--- /dev/null
+++ b/signal/tensorflow_core/ops/overlap_add_op.cc
@@ -0,0 +1,85 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+using tensorflow::shape_inference::InferenceContext;
+using tensorflow::shape_inference::ShapeHandle;
+
+namespace tensorflow {
+namespace signal {
+
+Status OverlapAddShape(InferenceContext* c) {
+  shape_inference::DimensionHandle unused;
+  ShapeHandle in;
+  ShapeHandle out;
+  int frame_step;
+
+  TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 2, &in));
+  TF_RETURN_IF_ERROR(c->GetAttr<int>("frame_step", &frame_step));
+  TF_RETURN_IF_ERROR(c->Subshape(in, 0, -1, &out));
+  if (!c->ValueKnown(c->Dim(out, -1))) {
+    TF_RETURN_IF_ERROR(c->ReplaceDim(out, -1, c->UnknownDim(), &out));
+  } else {
+    int n_frames = c->Value(c->Dim(out, -1));
+    TF_RETURN_IF_ERROR(
+        c->ReplaceDim(out, -1, c->MakeDim(n_frames * frame_step), &out));
+  }
+  c->set_output(0, out);
+  return OkStatus();
+}
+
+// TODO(b/286250473): change back name after name clash resolved
+REGISTER_OP("SignalOverlapAdd")
+    .Attr("T: {float, int16}")
+    .Attr("frame_step: int >= 1")
+    .Input("input: T")
+    .Output("output: T")
+    .SetShapeFn([](InferenceContext* c) { return OverlapAddShape(c); })
+    .Doc(R"doc(
+Transform a an input signal made of overlaping frames of size frame_size into
+an output signal made of frames of size frame_step.
+The overalpping input frames are spaced frame_step apart in time.
+The The op adds the overlapping frames into the output frame.
+For example, for a series of 5 input frames, with frame_size=3, frame_step=2:
+[1, 2, 3]
+[4, 5, 6]
+[7, 8, 9]
+[0, 1, 2]
+[3, 4, 5]
+The op will overlap the frames as follows:
+[1, 2, 3]
+      [4, 5, 6]
+            [7, 8, 9]
+                  [0, 1, 2]
+                        [3, 4, 5]
+Then add the samples that are aligned vertically to produce output frames of
+size frame_step=2:
+[1, 2]
+[7, 5]
+[13, 8]
+[9, 1]
+[5, 4]
+
+input: A [..., frames, frame_length] Tensor. Rank must be at least 2.
+output: A Tensor with shape [..., output_size] containing the overlap-added frames of signal's inner-most two dimensions.
+
+frame_step: The number of output samples to progress the 'current'
+sample on each invocation. Also the number of samples in each output frame.
+)doc");
+
+}  // namespace signal
+}  // namespace tensorflow