Automatic determination of multimedia and voice signals
View Patent ↗The present invention relates to means and methods of classifying speech and music signals in voice communication systems, devices, telephones, and methods, and more specifically, to systems, devices, and methods that automate control when either speech or music is detected over communication links. The present invention provides a novel system and method for monitoring the audio signal, analyze selected audio signal components, compare the results of analysis with a pre-determined threshold value, and classify the audio signal either as speech or music.
1. A method of manipulating sound signal, the method comprising the steps of:
a) obtaining a buffer of N samples of a sound signal;
b) passing the buffer of N samples through a high pass filter (HPF), with the HPF having a predetermined cut-off frequency in the range of 20 Hz to 800 Hz;
c) finding the power of the buffer of N samples using the equation:
pwr
=
1
N
∑
k
=
0
N
x
(
k
)
*
x
(
k
)
where N is the number of samples in the buffer and k is the time index;
d) averaging the power over a period of time where power is expressed as dB or as level and is calculated as
level
=
10
log
10
∑
i
=
0
N
DEC
pwr
(
i
)
where N DEC is the number of buffers considered per decision;
the signal passed through the HPF is processed by a voice activity detection device (VAD) to determine if the result is speech or a pause, in the event the input from part d is a pause, power calculated is expressed as pwr_sil, where the power is then averaged over a period of time, and expressed in dB as level_sil which is mathematically equal to:
level_sil=10 log 10 pwr_sil
the power value (dB) is then exponentially averaged using the equation:
level sil avg =α*level sil avg +(1−α)*level_sil, wherein α is a value between 0.01 and 0.99
e) the signal passed through the HPF is used as an input sequence x(n) in a Goertzel calculation s(n)=x(n)+2 cos(2πω)s(n−1)−s(n−2) to compute a sequence, s(n), the resulting sequence, s(n) is used to compute the DFTs at different frequencies (ω);
f) the DFTs are altered to equal their absolute value and then stored in an array N_DEC×M wherein M equals the number of pre-selected frequencies of musical notes;
g) histograms depicting energy distribution for each pre-selected frequency of musical notes are calculated and histograms bins with a higher value as compared to a pre-selected threshold are then summed and stored in a 1×M element array;
h) a difference signal is calculated by taking the first difference between adjacent elements in the array depicted in step (g);
i) calculating the standard deviation of the difference signal;
j) selecting a bottom threshold for the power level;
k) if the standard deviation of the difference signal is greater than the selected threshold (between 6 and 8), the signal is deemed to be a music signal, otherwise the signal is deemed to be speech or a pause, wherein a pause decision is based on the average power value for silence level sil avg .
2. The method of claim 1 wherein N is between 512 to 1024 samples.
3. The method of claim 2 wherein N_DEC is between 50 to 100 buffers.
4. The method of claim 3 wherein K, the time index, is between the values of 1 and N, wherein N is in the range of 512 to 1024.
5. The method of claim 4 wherein M, the number of pre-selected frequencies of musical notes is in the range of 12 to 120.
6. The method of claim 5 wherein the pre-selected frequencies of musical notes are in the frequency ranges of 20 Hz to 20,000 Hz.