Searched refs:estimate (Results 1 – 8 of 8) sorted by relevance
| /tflite-micro-3.4.0-2.7.6/tensorflow/lite/experimental/microfrontend/lib/ |
| D | noise_reduction.c | 28 uint32_t estimate = in NoiseReductionApply() local
30 ((uint64_t)state->estimate[i] * one_minus_smoothing)) >> in NoiseReductionApply()
32 state->estimate[i] = estimate; in NoiseReductionApply()
35 if (estimate > signal_scaled_up) { in NoiseReductionApply()
36 estimate = signal_scaled_up; in NoiseReductionApply()
43 (signal_scaled_up - estimate) >> state->smoothing_bits; in NoiseReductionApply()
50 memset(state->estimate, 0, sizeof(*state->estimate) * state->num_channels); in NoiseReductionReset()
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| D | noise_reduction_util.c | 35 state->estimate = calloc(state->num_channels, sizeof(*state->estimate)); in NoiseReductionPopulateState()
36 if (state->estimate == NULL) { in NoiseReductionPopulateState()
44 free(state->estimate); in NoiseReductionFreeStateContents()
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| D | pcan_gain_control_test.cc | 44 uint32_t estimate[] = {6321887, 31248341}; in TF_LITE_MICRO_TEST() local
48 &config.config_, &state, estimate, kNumChannels, kSmoothingBits, in TF_LITE_MICRO_TEST()
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| D | noise_reduction.h | 33 uint32_t* estimate; member
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| D | noise_reduction_test.cc | 55 TF_LITE_MICRO_EXPECT_EQ(state.estimate[i], expected[i]); in TF_LITE_MICRO_TEST()
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| D | frontend_util.c | 62 state->noise_reduction.estimate, state->filterbank.num_channels, in FrontendPopulateState()
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| /tflite-micro-3.4.0-2.7.6/tensorflow/lite/micro/examples/micro_speech/micro_features/ |
| D | micro_features_generator.cc | 66 g_micro_features_state.noise_reduction.estimate[i] = estimate_presets[i]; in SetMicroFeaturesNoiseEstimates()
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| /tflite-micro-3.4.0-2.7.6/tensorflow/lite/micro/examples/person_detection/ |
| D | training_a_model.md | 194 this case we're nearly five percent complete. The steps per second estimate is
195 also useful, since you can use it to estimate a rough duration for the whole
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