VAD working
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@ -27,7 +27,8 @@ fun CountLeadingZeros32(n: UInt): Int {
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// Normalize n by rounding up to the nearest number that is a sequence of 0
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// bits followed by a sequence of 1 bits. This number has the same number of
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// leading zeros as the original n. There are exactly 33 such values.
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var normalized = n or (n shr 1)
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var normalized = n
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normalized = normalized or (normalized shr 1)
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normalized = normalized or (normalized shr 2)
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normalized = normalized or (normalized shr 4)
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normalized = normalized or (normalized shr 8)
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@ -318,7 +319,7 @@ class VadInstT(aggressiveness: Aggressiveness = Aggressiveness.Quality) {
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// PDF parameters
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var noise_means = kNoiseDataMeans.clone()
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var speech_means = kSpeechDataStds.clone()
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var speech_means = kSpeechDataMeans.clone()
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var noise_stds = kNoiseDataStds.clone()
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var speech_stds = kSpeechDataStds.clone()
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@ -489,7 +490,7 @@ fun GmmProbability(self: VadInstT, features: List<Int>, total_power: Int, frame_
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}
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// Calculate local speech probabilities used later when updating the GMM.
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val h1 = (h1_test shr 12) // Q15
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val h1 = h1_test shr 12 // Q15
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if (h1 > 0) {
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// High probability of speech. Assign conditional probabilities for each
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// Gaussian in the GMM. Otherwise use the initialized values, i.e., 0.
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@ -530,7 +531,7 @@ fun GmmProbability(self: VadInstT, features: List<Int>, total_power: Int, frame_
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// (Q14 * Q11 shr 11) = Q14.
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val delt = (ngprvec[gaussian] * deltaN[gaussian]) shr 11
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// Q7 + (Q14 * Q15 shr 22) = Q7.
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nmk2 = nmk + (delt * kNoiseUpdateConst) shr 22
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nmk2 = nmk + ((delt * kNoiseUpdateConst) shr 22)
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}
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// Long term correction of the noise mean.
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@ -574,7 +575,7 @@ fun GmmProbability(self: VadInstT, features: List<Int>, total_power: Int, frame_
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self.speech_means[gaussian] = smk2 // Q7.
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// (Q7 shr 3) = Q4. With rounding.
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tmp_s16 = ((smk + 4) shr 3)
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tmp_s16 = (smk + 4) shr 3
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tmp_s16 = features[channel] - tmp_s16 // Q4
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// (Q11 * Q4 shr 3) = Q12.
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@ -737,15 +738,15 @@ fun FindMinimum(self: VadInstT, feature_value: Int, channel: Int): Int {
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val offset = channel * 16
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// Accessor for the age of each value of the [channel]
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val age = object {
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inline operator fun get(i: Int) = self.index_vector[offset + i]
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inline operator fun set(i: Int, value: Int) { self.index_vector[offset + i] = value }
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val age = object { // TODO: Inline?
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operator fun get(i: Int) = self.index_vector[offset + i]
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operator fun set(i: Int, value: Int) { self.index_vector[offset + i] = value }
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}
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// Accessor for the 16 minimum values of the [channel]
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val smallest_values = object {
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inline operator fun get(i: Int) = self.low_value_vector[offset + i]
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inline operator fun set(i: Int, value: Int) { self.low_value_vector[offset + i] = value }
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val smallest_values = object { // TODO: Inline?
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operator fun get(i: Int) = self.low_value_vector[offset + i]
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operator fun set(i: Int, value: Int) { self.low_value_vector[offset + i] = value }
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}
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assert(channel < kNumChannels)
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@ -757,7 +758,7 @@ fun FindMinimum(self: VadInstT, feature_value: Int, channel: Int): Int {
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age[i]++
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} else {
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// Too old value. Remove from memory and shift larger values downwards.
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for (j in i until 16) {
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for (j in i until 15) {
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smallest_values[j] = smallest_values[j + 1]
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age[j] = age[j + 1]
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}
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@ -925,13 +926,13 @@ fun AllPassFilter(input: AudioBuffer, filter_coefficient: Int, filter_state: Mut
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// First 6 taps of the impulse response:
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// 0.6399 0.5905 -0.3779 0.2418 -0.1547 0.0990
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val result = SampleArray(input.size / 2)
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val result = SampleArray((input.size + 1) / 2)
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var state32 = filter_state.toInt() * (1 shl 16) // Q15
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for (i in 0 until input.size step 2) {
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val tmp32 = state32 + filter_coefficient * input[i]
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val tmp16 = tmp32 shr 16 // Q(-1)
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result[i / 2] = tmp16.toShort()
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state32 = (input[i] * (1 shl 14)) - filter_coefficient * tmp16 // Q14
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state32 = input[i] * (1 shl 14) - filter_coefficient * tmp16 // Q14
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state32 *= 2 // Q15.
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}
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filter_state.setValue(state32 shr 16) // Q(-1)
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@ -1045,7 +1046,7 @@ fun LogOfEnergy(input: AudioBuffer, offset: Int, total_energy: MutableInt): Int
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// [kLogConst] is in Q9, [log2_energy] in Q10 and [tot_rshifts] in Q0.
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// Note that we in our derivation above have accounted for an output in Q4.
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var log_energy = (((kLogConst * log2_energy) shr 19) + (tot_rshifts * kLogConst) shr 9)
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var log_energy = ((kLogConst * log2_energy) shr 19) + ((tot_rshifts * kLogConst) shr 9)
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if (log_energy < 0) {
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log_energy = 0
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@ -1203,5 +1204,6 @@ fun ProcessVad(self: VadInstT, fs: Int, audio_frame: AudioBuffer): Boolean {
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assert(fs == 8000)
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val vad = CalcVad8khz(self, audio_frame)
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return vad != 0
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// return vad != 0
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return vad == 1
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}
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