diff --git a/tensorflow/core/kernels/spectrogram.cc b/tensorflow/core/kernels/spectrogram.cc
index 640cf7447f8..07eff7b45b0 100644
--- a/tensorflow/core/kernels/spectrogram.cc
+++ b/tensorflow/core/kernels/spectrogram.cc
@@ -64,19 +64,35 @@ bool Spectrogram::Initialize(const std::vector<double>& window,
   output_frequency_channels_ = 1 + fft_length_ / 2;
 
   // Allocate 2 more than what rdft needs, so we can rationalize the layout.
-  fft_input_output_.assign(fft_length_ + 2, 0.0);
+  fft_input_output_.resize(fft_length_ + 2);
 
   int half_fft_length = fft_length_ / 2;
-  fft_double_working_area_.assign(half_fft_length, 0.0);
-  fft_integer_working_area_.assign(2 + static_cast<int>(sqrt(half_fft_length)),
-                                   0);
+  fft_double_working_area_.resize(half_fft_length);
+  fft_integer_working_area_.resize(2 + static_cast<int>(sqrt(half_fft_length)));
+  initialized_ = true;
+  if (!Reset()) {
+    LOG(ERROR) << "Failed to Reset()";
+    return false;
+  }
+  return true;
+}
+
+bool Spectrogram::Reset() {
+  if (!initialized_) {
+    LOG(ERROR) << "Initialize() has to be called, before Reset().";
+    return false;
+  }
+  std::fill(fft_double_working_area_.begin(), fft_double_working_area_.end(),
+            0.0);
+  std::fill(fft_integer_working_area_.begin(), fft_integer_working_area_.end(),
+            0);
+
   // Set flag element to ensure that the working areas are initialized
   // on the first call to cdft.  It's redundant given the assign above,
   // but keep it as a reminder.
   fft_integer_working_area_[0] = 0;
   input_queue_.clear();
   samples_to_next_step_ = window_length_;
-  initialized_ = true;
   return true;
 }
 
diff --git a/tensorflow/core/kernels/spectrogram.h b/tensorflow/core/kernels/spectrogram.h
index fef0e649428..62544f575de 100644
--- a/tensorflow/core/kernels/spectrogram.h
+++ b/tensorflow/core/kernels/spectrogram.h
@@ -56,6 +56,19 @@ class Spectrogram {
   // Initialize with an explicit window instead of a length.
   bool Initialize(const std::vector<double>& window, int step_length);
 
+  // Reset internal variables.
+  // Spectrogram keeps internal state: remaining input data from previous call.
+  // As a result it can produce different number of frames when you call
+  // ComputeComplexSpectrogram multiple times (even though input data
+  // has the same size). As it is shown in
+  // MultipleCallsToComputeComplexSpectrogramMayYieldDifferentNumbersOfFrames
+  // in tensorflow/core/kernels/spectrogram_test.cc.
+  // But if you need to compute Spectrogram on input data without keeping
+  // internal state (and clear remaining input data from the previous call)
+  // you have to call Reset() before computing Spectrogram.
+  // For example in tensorflow/core/kernels/spectrogram_op.cc
+  bool Reset();
+
   // Processes an arbitrary amount of audio data (contained in input)
   // to yield complex spectrogram frames. After a successful call to
   // Initialize(), Process() may be called repeatedly with new input data
diff --git a/tensorflow/core/kernels/spectrogram_op.cc b/tensorflow/core/kernels/spectrogram_op.cc
index 8e64596b532..7977b3eac64 100644
--- a/tensorflow/core/kernels/spectrogram_op.cc
+++ b/tensorflow/core/kernels/spectrogram_op.cc
@@ -72,6 +72,9 @@ class AudioSpectrogramOp : public OpKernel {
 
     std::vector<float> input_for_channel(sample_count);
     for (int64 channel = 0; channel < channel_count; ++channel) {
+      OP_REQUIRES(context, spectrogram.Reset(),
+                  errors::InvalidArgument("Failed to Reset()"));
+
       float* output_slice =
           output_flat + (channel * output_height * output_width);
       for (int i = 0; i < sample_count; ++i) {
diff --git a/tensorflow/core/kernels/spectrogram_op_test.cc b/tensorflow/core/kernels/spectrogram_op_test.cc
index d34a7c99ecb..380c51e6508 100644
--- a/tensorflow/core/kernels/spectrogram_op_test.cc
+++ b/tensorflow/core/kernels/spectrogram_op_test.cc
@@ -101,6 +101,45 @@ TEST(SpectrogramOpTest, SquaredTest) {
       test::AsTensor<float>({0, 1, 4, 1, 0}, TensorShape({1, 1, 5})), 1e-3);
 }
 
+TEST(SpectrogramOpTest, MultichannelTest) {
+  Scope root = Scope::NewRootScope();
+
+  const int audio_size = 8;
+  const int channel_size = 2;
+  Tensor audio_tensor(DT_FLOAT, TensorShape({audio_size, channel_size}));
+  test::FillValues<float>(
+      &audio_tensor, {-1.0f, -1.0f, 0.0f, 0.0f, 1.0f, 1.0f, 0.0f, 0.0f, -1.0f,
+                      -1.0f, 0.0f, 0.0f, 1.0f, 1.0f, 0.0f, 0.0f});
+
+  Output audio_const_op = Const(root.WithOpName("audio_const_op"),
+                                Input::Initializer(audio_tensor));
+
+  AudioSpectrogram spectrogram_op =
+      AudioSpectrogram(root.WithOpName("spectrogram_op"), audio_const_op,
+                       audio_size, channel_size);
+
+  TF_ASSERT_OK(root.status());
+
+  ClientSession session(root);
+  std::vector<Tensor> outputs;
+
+  TF_EXPECT_OK(session.Run(ClientSession::FeedType(),
+                           {spectrogram_op.spectrogram}, &outputs));
+
+  const Tensor& spectrogram_tensor = outputs[0];
+
+  EXPECT_EQ(3, spectrogram_tensor.dims());
+  EXPECT_EQ(5, spectrogram_tensor.dim_size(2));
+  EXPECT_EQ(1, spectrogram_tensor.dim_size(1));
+  EXPECT_EQ(channel_size, spectrogram_tensor.dim_size(0));
+
+  for (int channel = 0; channel < channel_size; channel++) {
+    test::ExpectTensorNear<float>(
+        spectrogram_tensor.SubSlice(channel),
+        test::AsTensor<float>({0, 1, 2, 1, 0}, TensorShape({1, 5})), 1e-3);
+  }
+}
+
 }  // namespace
 }  // namespace ops
 }  // namespace tensorflow