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/*
* 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 "modules/audio_processing/rms_level.h"
#include <algorithm>
#include <cmath>
#include <numeric>
#include "rtc_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 * std::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_ = std::nullopt;
}
void RmsLevel::Analyze(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::Analyze(ArrayView<const float> data) {
if (data.empty()) {
return;
}
CheckBlockSize(data.size());
float sum_square = 0.f;
for (float data_k : data) {
int16_t tmp =
static_cast<int16_t>(std::min(std::max(data_k, -32768.f), 32767.f));
sum_square += tmp * tmp;
}
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() {
const bool have_samples = (sample_count_ != 0);
int rms = have_samples ? ComputeRms(sum_square_ / sample_count_)
: RmsLevel::kMinLevelDb;
// To ensure that kMinLevelDb represents digital silence (muted audio
// sources) we'll check here if the sum_square is actually 0. If it's not
// we'll bump up the return value to `kInaudibleButNotMuted`.
// https://datatracker.ietf.org/doc/html/rfc6464
if (have_samples && rms == RmsLevel::kMinLevelDb && sum_square_ != 0.0f) {
rms = kInaudibleButNotMuted;
}
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 std::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_ != block_size) {
Reset();
block_size_ = block_size;
}
}
} // namespace webrtc
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