public string RecognizeByAudioSource(AudioSource audioSource)
{
string result = null;
audioSource.GetOutputData(playingAudioData, 0);
audioSource.GetSpectrumData(playingAudioSpectrum, 0, FFTWindow.BlackmanHarris);
if (audioSource.isPlaying == true)
{
amplitudeSum = 0.0f;
for (int i = 0; i < playingAudioSpectrum.Length; ++i)
{
amplitudeSum += playingAudioSpectrum[i];
}
if (amplitudeSum >= amplitudeThreshold)
{
MathToolBox.Convolute(playingAudioSpectrum, gaussianFilter, MathToolBox.EPaddleType.Repeat, smoothedAudioSpectrum);
MathToolBox.FindLocalLargestPeaks(smoothedAudioSpectrum, peakValues, peakPositions);
frequencyUnit = audioSource.clip.frequency / 2 / windowSize;
for (int i = 0; i < formantArray.Length; ++i)
{
formantArray[i] = peakPositions[i] * frequencyUnit;
}
// TODO: Recognization by multiple formant
switch (recognizingLanguage)
{
case ERecognizerLanguage.Japanese:
currentVowels = vowelsByFormantJP;
currentVowelFormantCeilValues = vowelFormantFloorJP;
break;
case ERecognizerLanguage.Chinese:
currentVowels = vowelsByFormantCN;
currentVowelFormantCeilValues = vowelFormantFloorCN;
break;
}
for (int i = 0; i < currentVowelFormantCeilValues.Length; ++i)
{
if (Average(formantArray) > currentVowelFormantCeilValues[i])
{
result = currentVowels[i];
//Debug.Log(Average(formantArray));
}
}
}
else
{
result = null;
}
}
else
{
result = null;
}
return(result);
}
public LipSyncRuntimeRecognizer(ERecognizerLanguage recognizingLanguage, int windowSize, float amplitudeThreshold)
{
this.recognizingLanguage = recognizingLanguage;
this.windowSize = Mathf.ClosestPowerOfTwo(windowSize);
this.playingAudioData = new float[this.windowSize];
this.playingAudioSpectrum = new float[this.windowSize];
this.amplitudeThreshold = amplitudeThreshold;
this.gaussianFilter = MathToolBox.GenerateGaussianFilter(FILTER_SIZE, FILTER_DEVIATION_SQUARE);
this.smoothedAudioSpectrum = new float[this.windowSize];
this.peakValues = new float[FORMANT_COUNT];
this.peakPositions = new int[FORMANT_COUNT];
this.formantArray = new float[FORMANT_COUNT];
}
/// <summary>
/// Convolute data and filter. Result is sent to output, which must not be shorter than data.
/// </summary>
/// <param name="output">Array to store output. Must not be shorter than data.</param>
/// <param name="data">Source data array.</param>
/// <param name="filter">Filter array.</param>
/// <param name="paddleType">Paddle type.</param>
public static void Convolute(float[] data, float[] filter, EPaddleType paddleType, float[] output)
{
int filterMiddlePoint = Mathf.FloorToInt(filter.Length / 2);
for (int n = 0; n < data.Length; ++n)
{
output[n] = 0.0f;
for (int m = 0; m < filter.Length; ++m)
{
output[n] +=
MathToolBox.GetValueFromArray(data, n - filterMiddlePoint + m, paddleType) *
filter[filter.Length - m - 1];
}
}
}
public LipSyncOfflineRecognizer(ERecognizerLanguage recognizingLanguage, float amplitudeThreshold, int windowSize, int shiftStepSize)
{
this.recognizingLanguage = recognizingLanguage;
this.windowSize = Mathf.ClosestPowerOfTwo(windowSize);
this.shiftStepSize = shiftStepSize;
this.amplitudeThreshold = amplitudeThreshold;
this.gaussianFilter = MathToolBox.GenerateGaussianFilter(FILTER_SIZE, FILTER_DEVIATION_SQUARE);
this.windowArray = MathToolBox.GenerateWindow(windowSize, MathToolBox.EWindowType.Hamming);
this.smoothedAudioSpectrum = new float[this.windowSize];
this.peakValues = new float[FORMANT_COUNT];
this.peakPositions = new int[FORMANT_COUNT];
this.formantArray = new float[FORMANT_COUNT];
}
/// <summary>
///
/// </summary>
/// <param name="audioClip"></param>
/// <returns></returns>
public string[] RecognizeAllByAudioClip(AudioClip audioClip)
{
int recognizeSampleCount = Mathf.CeilToInt((float)(audioClip.samples) / (float)(shiftStepSize));
string[] result = new string[recognizeSampleCount];
float[] currentAudioData = new float[this.windowSize];
float[] currentAudioSpectrum = new float[this.windowSize];
for (int i = 0; i < recognizeSampleCount; ++i)
{
audioClip.GetData(currentAudioData, i * shiftStepSize);
for (int j = 0; j < windowSize; ++j)
{
currentAudioData[j] *= windowArray[j];
}
currentAudioSpectrum = MathToolBox.DiscreteCosineTransform(currentAudioData);
amplitudeSum = 0.0f;
for (int k = 0; k < windowSize; ++k)
{
amplitudeSum += currentAudioSpectrum[k];
}
if (amplitudeSum >= amplitudeThreshold)
{
MathToolBox.Convolute(currentAudioSpectrum, gaussianFilter, MathToolBox.EPaddleType.Repeat, smoothedAudioSpectrum);
MathToolBox.FindLocalLargestPeaks(smoothedAudioSpectrum, peakValues, peakPositions);
frequencyUnit = audioClip.frequency / 2 / windowSize;
for (int l = 0; l < formantArray.Length; ++l)
{
formantArray[l] = peakPositions[l] * frequencyUnit;
}
switch (recognizingLanguage)
{
case ERecognizerLanguage.Japanese:
currentVowels = vowelsByFormantJP;
currentVowelFormantCeilValues = vowelFormantFloorJP;
break;
case ERecognizerLanguage.Chinese:
currentVowels = vowelsByFormantCN;
currentVowelFormantCeilValues = vowelFormantFloorCN;
break;
}
for (int m = 0; m < currentVowelFormantCeilValues.Length; ++m)
{
if (formantArray[0] > currentVowelFormantCeilValues[m])
{
result[i] = currentVowels[m];
}
}
}
else
{
result[i] = null;
}
}
return(result);
}