|  | /* | 
|  | *  Copyright (c) 2014 The WebRTC project authors. All Rights Reserved. | 
|  | * | 
|  | *  Use of this source code is governed by a BSD-style license | 
|  | *  that can be found in the LICENSE file in the root of the source | 
|  | *  tree. An additional intellectual property rights grant can be found | 
|  | *  in the file PATENTS.  All contributing project authors may | 
|  | *  be found in the AUTHORS file in the root of the source tree. | 
|  | */ | 
|  |  | 
|  | #include "webrtc/modules/audio_processing/rms_level.h" | 
|  |  | 
|  | #include <math.h> | 
|  | #include <algorithm> | 
|  | #include <numeric> | 
|  |  | 
|  | #include "webrtc/base/checks.h" | 
|  |  | 
|  | namespace webrtc { | 
|  | namespace { | 
|  | static constexpr float kMaxSquaredLevel = 32768 * 32768; | 
|  | // kMinLevel is the level corresponding to kMinLevelDb, that is 10^(-127/10). | 
|  | static constexpr float kMinLevel = 1.995262314968883e-13f; | 
|  |  | 
|  | // Calculates the normalized RMS value from a mean square value. The input | 
|  | // should be the sum of squared samples divided by the number of samples. The | 
|  | // value will be normalized to full range before computing the RMS, wich is | 
|  | // returned as a negated dBfs. That is, 0 is full amplitude while 127 is very | 
|  | // faint. | 
|  | int ComputeRms(float mean_square) { | 
|  | if (mean_square <= kMinLevel * kMaxSquaredLevel) { | 
|  | // Very faint; simply return the minimum value. | 
|  | return RmsLevel::kMinLevelDb; | 
|  | } | 
|  | // Normalize by the max level. | 
|  | const float mean_square_norm = mean_square / kMaxSquaredLevel; | 
|  | RTC_DCHECK_GT(mean_square_norm, kMinLevel); | 
|  | // 20log_10(x^0.5) = 10log_10(x) | 
|  | const float rms = 10.f * log10(mean_square_norm); | 
|  | RTC_DCHECK_LE(rms, 0.f); | 
|  | RTC_DCHECK_GT(rms, -RmsLevel::kMinLevelDb); | 
|  | // Return the negated value. | 
|  | return static_cast<int>(-rms + 0.5f); | 
|  | } | 
|  | }  // namespace | 
|  |  | 
|  | RmsLevel::RmsLevel() { | 
|  | Reset(); | 
|  | } | 
|  |  | 
|  | RmsLevel::~RmsLevel() = default; | 
|  |  | 
|  | void RmsLevel::Reset() { | 
|  | sum_square_ = 0.f; | 
|  | sample_count_ = 0; | 
|  | max_sum_square_ = 0.f; | 
|  | block_size_ = rtc::Optional<size_t>(); | 
|  | } | 
|  |  | 
|  | void RmsLevel::Analyze(rtc::ArrayView<const int16_t> data) { | 
|  | if (data.empty()) { | 
|  | return; | 
|  | } | 
|  |  | 
|  | CheckBlockSize(data.size()); | 
|  |  | 
|  | const float sum_square = | 
|  | std::accumulate(data.begin(), data.end(), 0.f, | 
|  | [](float a, int16_t b) { return a + b * b; }); | 
|  | RTC_DCHECK_GE(sum_square, 0.f); | 
|  | sum_square_ += sum_square; | 
|  | sample_count_ += data.size(); | 
|  |  | 
|  | max_sum_square_ = std::max(max_sum_square_, sum_square); | 
|  | } | 
|  |  | 
|  | void RmsLevel::AnalyzeMuted(size_t length) { | 
|  | CheckBlockSize(length); | 
|  | sample_count_ += length; | 
|  | } | 
|  |  | 
|  | int RmsLevel::Average() { | 
|  | int rms = (sample_count_ == 0) ? RmsLevel::kMinLevelDb | 
|  | : ComputeRms(sum_square_ / sample_count_); | 
|  | Reset(); | 
|  | return rms; | 
|  | } | 
|  |  | 
|  | RmsLevel::Levels RmsLevel::AverageAndPeak() { | 
|  | // Note that block_size_ should by design always be non-empty when | 
|  | // sample_count_ != 0. Also, the * operator of rtc::Optional enforces this | 
|  | // with a DCHECK. | 
|  | Levels levels = (sample_count_ == 0) | 
|  | ? Levels{RmsLevel::kMinLevelDb, RmsLevel::kMinLevelDb} | 
|  | : Levels{ComputeRms(sum_square_ / sample_count_), | 
|  | ComputeRms(max_sum_square_ / *block_size_)}; | 
|  | Reset(); | 
|  | return levels; | 
|  | } | 
|  |  | 
|  | void RmsLevel::CheckBlockSize(size_t block_size) { | 
|  | if (block_size_ != rtc::Optional<size_t>(block_size)) { | 
|  | Reset(); | 
|  | block_size_ = rtc::Optional<size_t>(block_size); | 
|  | } | 
|  | } | 
|  | }  // namespace webrtc |