Abstract:
Presented herein are systems and methods for classifying an audio signal. The audio signal is classified by calculating a plurality of linear prediction coefficients (LPC) for a portion of the audio signal; inverse filtering the portion of the audio signal with the plurality of linear prediction coefficients (LPC), thereby resulting in a residual signal; measuring the residual energy of the residual signal; and comparing the residual energy to a threshold.

Description:
FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT  
       [0001]     [Not Applicable] 
       [MICROFICHE/COPYRIGHT REFERENCE] 
       [0002]     [Not Applicable] 
       BACKGROUND OF THE INVENTION  
       [0003]     Human beings, with normal hearing, are often able to distinguish sounds from about 20 Hz, such as the lowest note on a large pipe organ, to 20,000 Hz, such as the high shrill of a dog whistle. Human speech, on the other hand, ranges from 300 Hz to 4,000 Hz.  
         [0004]     Music may be produced by playing musical instruments. Musical instruments often produce sounds that lie outside the range of human speech, and in many instances, produce sounds (overtones, etc.) that lie outside the range of human hearing.  
         [0005]     An audio communication can comprise either music, speech or both. However, conventional equipment processes audio communication signals comprising only speech in a similar manner as communication signals comprising music.  
         [0006]     Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of such systems with embodiments presented in the remainder of the present application with references to the drawings.  
       SUMMARY OF THE INVENTION  
       [0007]     Presented herein are systems and methods for classifying an audio signal.  
         [0008]     In one embodiment of the present invention, there is presented a method for classifying an audio signal. The method comprises calculating a plurality of linear prediction coefficients for a portion of the audio signal; inverse filtering the portion of the audio signal with the plurality of linear prediction coefficients filter, thereby resulting in a residual signal; measuring the energy of the residual signal; and comparing the residual energy to a threshold.  
         [0009]     In another embodiment, the method further comprises classifying the portion of the audio signal as music, if the residual energy exceeds the threshold; and classifying the portion of the audio signal as speech, if the threshold exceeds the residual energy.  
         [0010]     In another embodiment, the portion of the audio signal comprises a frame.  
         [0011]     In another embodiment, the method further comprises decimating the frame, thereby causing the frame to comprise a predetermined number of samples.  
         [0012]     In another embodiment, the method further comprises spectrally flattening the portion of the audio signal.  
         [0013]     In another embodiment, there is presented a method for classifying an audio signal.  
         [0014]     The method comprises taking a discrete Fourier transformation of a portion of the audio signal for a plurality of frequencies; calculating a plurality of linear prediction coefficients (LPC) for the portion of the signal; measuring an inverse filter response for said plurality of frequencies with said plurality of linear prediction coefficients (LPC); measuring a mean squared error between the discrete Fourier transformation of the portion of the audio signal for the plurality of frequencies and the inverse filter response; and comparing the means squared error to a threshold.  
         [0015]     In another embodiment, the method further comprises classifying the portion of the audio signal as music, if the mean squared error exceeds the threshold; and classifying the portion of the audio signal as speech, if the threshold exceeds the means squared error energy.  
         [0016]     In another embodiment, the portion of the audio signal comprises a frame.  
         [0017]     In another embodiment, the method further comprises decimating the frame, thereby causing the frame to comprise a predetermined number of samples.  
         [0018]     In another embodiment, the method further comprises spectrally flattening the portion of the audio signal.  
         [0019]     In another embodiment, there is presented a system for classifying an audio signal. The system comprises a first circuit, an inverse filter, a second circuit, and a third circuit. The first circuit calculates a plurality of linear prediction coefficients for a portion of the audio signal. The inverse filter inverse filters the portion of the audio signal with the plurality of linear prediction coefficients, thereby resulting in a residual signal. The second circuit measures the energy of the residual signal. The third circuit compares the residual energy to a threshold.  
         [0020]     In another embodiment, the system further comprises logic for classifying the portion of the audio signal as music, if the residual energy exceeds the threshold, and classifying the portion of the audio signal as speech, if the threshold exceeds the residual energy value.  
         [0021]     In another embodiment, the portion of the audio signal comprises a frame.  
         [0022]     In another embodiment, the system further comprises a decimator for decimating the frame, thereby causing the frame to comprise a predetermined number of samples.  
         [0023]     In another embodiment, the system further comprises a pre-emphasis filter for spectrally flattening the portion of the audio signal.  
         [0024]     In another embodiment, there is presented a system for classifying an audio signal. The system comprises a first circuit, a second circuit, an inverse filter, a third circuit, and a fourth circuit. The first circuit takes a discrete Fourier transformation of a portion of the audio signal for a plurality of frequencies. The second circuit calculates a plurality of linear prediction coefficients (LPC) for the same portion of the signal. The inverse filter measures an inverse filter response for said plurality of frequencies with said plurality of linear prediction coefficients (LPC). The third circuit measures a mean squared error between the discrete Fourier transformation of the portion of the audio signal for the plurality of frequencies and the inverse filter response. The fourth circuit compares the means squared error to a threshold.  
         [0025]     In another embodiment, the system further comprises logic for classifying the portion of the audio signal as music, if the mean squared error exceeds the threshold and classifying the portion of the audio signal as speech, if the threshold exceeds the means squared error energy. In another embodiment, the portion of the audio signal comprises a frame.  
         [0026]     In another embodiment, the system further comprises a decimator for decimating the frame, thereby causing the frame to comprise a predetermined number of samples.  
         [0027]     In another embodiment, the system further comprises a pre-emphasis filter for spectrally flattening the portion of the audio signal.  
         [0028]     These and other advantages and novel features of the present invention, as well as details of an illustrated example embodiment thereof, will be more fully understood from the following description and drawings.  
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0029]      FIG. 1  is a flow diagram for classifying a digital audio signal as speech or music in accordance with an embodiment of the present invention;  
         [0030]      FIG. 2  is a flow diagram for classifying a digital audio signal as speech or music in accordance with an alternative embodiment of the present invention;  
         [0031]      FIG. 3  is a system for classifying a digital audio signal as speech or music in accordance with an embodiment of the present invention;  
         [0032]      FIG. 4  is a system for classifying a digital audio signal as speech or music in accordance with an alternative embodiment of the present invention;  
         [0033]      FIG. 5  is a block diagram illustrating a system for converting, classifying, encoding, and packetizing an audio communication according to an embodiment of the present invention;  
         [0034]      FIG. 6  is a block diagram illustrating encoding of an exemplary audio signal according to an embodiment of the present invention; and  
         [0035]      FIG. 7  is a block diagram illustrating an exemplary audio decoder according to an embodiment of the present invention.  
     
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0036]     Referring now to  FIG. 1 , there is illustrated a flow diagram for classifying whether a digital audio signal is speech or music. At  105 , the digital audio signal is divided into a set of frames. The frames comprise a fixed number of digital audio samples from the digital audio signal. Additionally, frames can be processed in a number of ways, such as by a decimator, pre-emphasis filter, or a windowing function, to name a few.  
         [0037]     At  110 , a finite number of Linear Prediction coefficients (LPC) are calculated for each frame. In general, the inherent limitations of the human vocal tract allow a speech signal spectrum to be shaped by fewer LPC coefficients than a music signal. Accordingly, at  115  the inverse filter response of the frame to an inverse filter according to the LPC coefficients (the residual signal) calculated during 110 is taken and the residual energy is measured at  117 . The residual energy of the filter response is compared at  120  to an energy threshold.  
         [0038]     If the residual energy exceeds the threshold, at  120 , the frame is classified ( 125 ) as music. If the residual energy does not exceed the threshold at  120 , the frame is classified ( 130 ) as speech.  
         [0039]     Referring now to  FIG. 2 , there is illustrated a flow diagram for classifying a digital audio signal as speech or music in accordance with an alternative embodiment of the present invention. At  55 , the digital audio signal is divided into a set of frames. The frames comprise a fixed number of digital audio samples from the digital audio signal. Additionally, frames can be processed in a number of ways, such as by a decimator, pre-emphasis filter, or a windowing function, to name a few.  
         [0040]     At  60 , the Discrete Fourier Transformation (DFT) is taken for a frame. At  65 , the LPC coefficients are determined. At  70 , the LPC inverse filter response is taken and measured for the DFT frequencies. At  75 , the mean squared error is calculated and compared to a threshold at  80 .  
         [0041]     If the means squared error exceeds the threshold, at  230 , the frame is classified ( 85 ) as music. If the mean squared error does not exceed the threshold at  80 , the frame is classified ( 90 ) as speech.  
         [0042]     Referring now to  FIG. 3 , there is illustrated a block diagram describing an exemplary system for classifying a digital audio input signal  105  as speech or music. The digital audio input signal  105  can be from any real time audio source or recorded data from any other medium.  
         [0043]     A decimator filter  110  receives the digital audio input signal  105  and divides the digital audio input signal  105  into smaller blocks containing a finite number of audio samples called a frame. The frame size depends upon the sampling rate of the digital audio input signal  105 , because the decimator filter  110  provides a fixed number of samples per frame, and a fixed number of frames per second. For example, if the digital audio input signal  105  is sampled at 48000 samples/second, and the decimator filter  110  provides 50 frames comprising 160 samples, per second, the frame size can be set at 960 samples per frame, and the decimation factor set at six. The decimator filter  110  can be an adaptive filter that decimates the given audio samples appropriately in such a way that the output of the decimator filter  110  is at a fixed rate.  
         [0044]     A pre-emphasis filter  115  receives the output  112  of the decimator filter  110 . The pre-emphasis filter  115  may be a first-order finite impulse response (FIR) filter that spectrally flattens the output  112  of the decimator filter  110 . The pre-emphasis filter can have the transfer function: 
 
 H ( z )=1/(1+a pre   z   −1 ) 
 
         [0045]     The pre-emphasis factor a pre  can be approximately 15/16. The pre-emphasis filter  115  removes the DC component of the audio signal and helps in improving the estimation of Linear Prediction Coefficients (LPC) from auto-correlation values.  
         [0046]     A windowing function  120  receives the output  117  of the pre-emphasis filter  115 . The windowing function  120  can comprise any one of a number of different windowing standards, such as, Hamming, Hanning, Blackman, or Kaiser windows. The individual frames are windowed to minimize the signal discontinuities at the borders of each frame. If the window is defined as w[n],  0 &lt;n&lt;N−1, then the windowed signal is s[n]=w[n]*u[n], where u[n] is the initial input data before windowing.  
         [0047]     An auto-correlation coefficients computation function  125  receives the output of the windowing function  120 . In an exemplary case, the windowed frame S comprises 160 samples, where S=(s(0), s(1) . . . s(159)). In a case where the frame comprises 160 samples, a 10 th  order LPC coding is sufficient to model the spectrum if S is a speech signal. The signal s[n] is related to the innovation u[n] signal [The error signal between the actual signal and signal predicted using this 10 th  order LPC coefficients] through the linear difference equation:  
           s   ⁡     (   n   )       +       ∑     i   =   1     10     ⁢           ⁢       a   i     ⁢     s   ⁡     (     n   -   i     )             =     u   ⁡     (   n   )           
 
         [0048]     These 10 LPC coefficients are chosen to minimize the energy of the innovation signal u[n]:  
       f   =       ∑     n   =   0     159     ⁢           ⁢       u   2     ⁡     (   n   )             
 
         [0049]     The foregoing can be determined by taking the derivative with respect to a i , and setting the derivative to zero as shown below: 
 
 df/da   1 =0 
 
 df/da   2 =0 
 
 df/da   10 =0 
 
         [0050]     The above can be simplified to get 10 linear equations with 10 unknowns, the unknowns being the LPC coefficients. The 10 equations can be represented by the martix below:  
         [           R   ⁡     (   0   )             R   ⁡     (   1   )             R   ⁡     (   2   )             R   ⁡     (   3   )             R   ⁡     (   4   )             R   ⁡     (   5   )             R   ⁡     (   6   )             R   ⁡     (   7   )             R   ⁡     (   8   )             R   ⁡     (   9   )                 R   ⁡     (   1   )             R   ⁡     (   0   )             R   ⁡     (   1   )             R   ⁡     (   2   )             R   ⁡     (   3   )             R   ⁡     (   4   )             R   ⁡     (   5   )             R   ⁡     (   6   )             R   ⁡     (   7   )             R   ⁡     (   8   )                 R   ⁡     (   2   )             R   ⁡     (   1   )             R   ⁡     (   0   )             R   ⁡     (   1   )             R   ⁡     (   2   )             R   ⁡     (   3   )             R   ⁡     (   4   )             R   ⁡     (   5   )             R   ⁡     (   6   )             R   ⁡     (   7   )                 R   ⁡     (   3   )             R   ⁡     (   2   )             R   ⁡     (   1   )             R   ⁡     (   0   )             R   ⁡     (   1   )             R   ⁡     (   2   )             R   ⁡     (   3   )             R   ⁡     (   4   )             R   ⁡     (   5   )             R   ⁡     (   6   )                 R   ⁡     (   4   )             R   ⁡     (   3   )             R   ⁡     (   2   )             R   ⁡     (   1   )             R   ⁡     (   0   )             R   ⁡     (   1   )             R   ⁡     (   2   )             R   ⁡     (   3   )             R   ⁡     (   4   )             R   ⁡     (   5   )                 R   ⁡     (   5   )             R   ⁡     (   4   )             R   ⁡     (   3   )             R   ⁡     (   2   )             R   ⁡     (   1   )             R   ⁡     (   0   )             R   ⁡     (   1   )             R   ⁡     (   2   )             R   ⁡     (   3   )             R   ⁡     (   4   )                 R   ⁡     (   6   )             R   ⁡     (   5   )             R   ⁡     (   4   )             R   ⁡     (   3   )             R   ⁡     (   2   )             R   ⁡     (   1   )             R   ⁡     (   0   )             R   ⁡     (   1   )             R   ⁡     (   2   )             R   ⁡     (   3   )                 R   ⁡     (   7   )             R   ⁡     (   6   )             R   ⁡     (   5   )             R   ⁡     (   4   )             R   ⁡     (   3   )             R   ⁡     (   2   )             R   ⁡     (   1   )             R   ⁡     (   0   )             R   ⁡     (   1   )             R   ⁡     (   2   )                 R   ⁡     (   8   )             R   ⁡     (   7   )             R   ⁡     (   6   )             R   ⁡     (   5   )             R   ⁡     (   4   )             R   ⁡     (   3   )             R   ⁡     (   2   )             R   ⁡     (   1   )             R   ⁡     (   0   )             R   ⁡     (   1   )                 R   ⁡     (   9   )             R   ⁡     (   8   )             R   ⁡     (   7   )             R   ⁡     (   6   )             R   ⁡     (   5   )             R   ⁡     (   4   )             R   ⁡     (   3   )             R   ⁡     (   2   )             R   ⁡     (   1   )             R   ⁡     (   0   )             ]     ⁢      ⁠   ⁢           [                   a   1               a   2               a   3               a   4               a   5               a   6               a   7               a   8               a   9               a   10           ]     =       [           -     R   ⁡     (   1   )                   -     R   ⁡     (   2   )                   -     R   ⁡     (   3   )                   -     R   ⁡     (   4   )                   -     R   ⁡     (   5   )                   -     R   ⁡     (   6   )                   -     R   ⁡     (   7   )                   -     R   ⁡     (   8   )                   -     R   ⁡     (   9   )                   -     R   ⁡     (   10   )               ]     ⁢     
     ⁢   Where   ⁢     
     ⁢             R   ⁡     (   k   )       =       ⁢       ∑     n   =   0       159   -   k       ⁢       s   ⁡     (   n   )       ⁢     s   ⁡     (     n   +   k     )                       =       ⁢     autocorrelation   ⁢           ⁢   of   ⁢           ⁢     s   ⁡     (   n   )                             
 
         [0051]     The auto-correlation coefficients computation function  125  provides the auto-correlation coefficients R(k) to the LPC coefficients computation function  130 . The LPC coefficients are determined by calculating a 1 , . . . a 10  from the above matrix. The above matrix can be solved using the Gaussian elimination method, matrix inversion, or Levinson-Durbin recursion. However, since the above matrix is a Toeplitz matrix (symmetrical &amp; diagonals equal), the standard Levinson-Durban recursion is advantageous.  
         [0052]     The LPC coefficients are provided from the LPC Coefficients Computation function  130  to an Inverse LPC Analysis Filter  135 . The LPC analysis filter filters the input data s[n]. Since a 10 th  order LPC filter response very closely represents the gross shape of a given input speech signal spectrum for a frame comprising 160 samples, if the given audio signal s[n] represents speech, the residual energy will be very small in comparison to the input audio signal energy. In contrast, if the given audio signal s[n] represents music, the residual energy will be significant in comparison to the input audio signal energy.  
               Input   ⁢           ⁢   signal   ⁢           ⁢   energy     =       ⁢       ∑     n   =   0       n   =   159       ⁢       s   2     ⁡     [   n   ]                       Residual   ⁢           ⁢   signal   ⁢           ⁢   energy     =       ⁢       ∑     n   =   0       n   =   159       ⁢       r   2     ⁡     [   n   ]                   
 
         [0053]     In some cases, it may not be easy to decide clearly about speech or music for a specific frame since the energy ratio value may be very close to the threshold value. In such cases, the decision may be delayed for few frames and final decision for all the frames is taken jointly depending upon the majority of the frame decisions. Each frame decision (i.e. speech or music) is taken the same way by comparing the ratio of the residual signal energy to input signal energy against the ENERGY_THRESHOLD (0.15) value for all the frames but final decision for all the audio frames is taken at the end only depending upon the majority of all the decisions.  
         [0054]     If the ratio of residual signal energy to input signal energy is very close to the ENERGY_THRESHOLD value then decision is delayed for that frame and the same algorithm is applied to the next two or four consecutive frames depending upon the energy ratio value. Once, the individual decision is taken for all the three/five frames. With majority logic  140 , whatever decisions (either speech or music) are more for all the frames, that same decision is applied to all three/five frames together.  
         [0055]     Referring now to  FIG. 4 , there is illustrated a block diagram of a system for classifying an input digital audio signal as music or speech in accordance with an alternative embodiment of the present invention. The Fourier transform of the given input signal s[n] is taken for a finite number of points and the magnitude of all 512 uniformly spaced frequency values are computed by a DFT function  145 . The LPC filter response also at all those same 512 frequency values is sampled and the magnitude of all those 512 frequency values are computed by LPC filter sampling function  150 .  
         [0056]     With the frequency magnitudes vector for all 512 frequencies from both DFT function  145  and LPC filter sampling function  150 , the mean squared error value for all the frequencies is computed by a means squared error computation function  155 . Once the mean squared value is computed, the value is compared against a SQUARED_ERROR_THRESHOLD value. If the value is below that threshold value, it will be declared a speech frame, otherwise it will be declared a music frame.  
         Mean   ⁢           ⁢   squared   ⁢           ⁢   error     =       1   512     ⁢       ∑     f   =   0       f   =   511       ⁢       [       S   ⁡     (   f   )       -     H   ⁡     (   f   )         ]     2             
 
         [0057]     In some cases, it may not be easy to decide clearly about speech or music for a specific frame since the mean squared error value may be very close to the threshold value. In such cases, the decision may be delayed for few frames and final decision for all the frames is taken jointly depending upon the majority logic  140 . It means that the frame decision (i.e. speech or music) is taken the same way by comparing the mean squared error value against the SQUARED_ERROR_THRESHOLD value for all the frames.  
         [0058]     If the ratio of mean squared error value is very close to SQUARED_ERROR_THRESHOLD value then decision is delayed for that frame and the same algorithm is applied to the next two or four consecutive frames depending upon the mean squared error value. The individual decision is taken for all the three/five frames one time.  
         [0059]      FIG. 5  is a block diagram illustrating a system  800 B for converting, classifying, encoding, and packetizing an audio communication according to an embodiment of the present invention. The system  800 B receives an audio communication  810 B, wherein the audio communication  810 B may be either an analog signal  801 B or a digital signal  803 B. The audio communication  810 B may proceed directly to speech/music classification apparatus  866 B as an analog signal  801 B at junction  863 B. Alternatively, the audio signal  810 B may be passed through analog to digital converter  805 B for conversion to a digital signal  803 B that is provided via junction  797  to the speech/music classification apparatus  866 B. After conversion from analog to digital, the digital signal  803 B may be passed to MPEG encoder  825 B. The circumstances of the audio signal processing at the MPEG encoder  852 B will be described below.  
         [0060]     The audio signal may arrive at the speech/music classifying apparatus  866 B at input  820 B. The signal is then passed to mathematical processor  830 B. After the mathematical processing has been completed and the ratio is determined, the ratio is passed to comparator  860 B. Comparator  860 B is adapted to compare the calculated ratio to the threshold value. The threshold value may be pre-set by a user, or the comparator  860 B may determine (learn) the threshold value through trial and error. If the ratio is greater than the threshold value, then the output from the speech/music classifying apparatus  866 B is that the audio signal is determined to be music. However, if the ratio is less than the threshold value, then the output from the classifying apparatus  866 B is that the audio signal is speech.  
         [0061]     The signal may then be passed to either encoder  825 B or alternatively to packetization engine  835 B via junction  895 B. In one embodiment, encoder  825   b  comprises an MPEG encoder. The encoder  825 B converts the digital signal  803 B to an audio elementary stream (AES), AES encoding the digital signal  803 B in accordance with the MPEG standard, for example. When the AES is directed to the packetization engine  835 B, the AES is packetized into a packetized audio elementary stream comprising packets  855 B. Each packet comprising a portion of the AES and may also comprises a flag  875 B. The flag  875 B may indicate that the portion of the AES in the packet is speech or music depending upon the state of the flag  875 B, i.e., whether the flag is turned on or off.  
         [0062]      FIG. 6  is a block diagram  800 C illustrating encoding of an exemplary audio signal A(t)  810 C by the encoder  825 B according to an embodiment of the present invention. The audio signal  810 C is sampled and the samples are grouped into frames  820 C (F 0  . . . F n ) of 1024 samples, e.g., (F x (0) . . . F x (1023)). The frames  820 C (F 0  . . . F n ) are grouped into windows  830 C (W 0  . . . W n ) that comprise 2048 samples or two frames, e.g., (W x (0) . . . W x (2047)). However, each window  830 C W x  has a 50% overlap with the previous window  830 C W x-1 .  
         [0063]     Accordingly, the first 1024 samples of a window  830 C W x  are the same as the last 1024 samples of the previous window  830 C W x-1 . A window function w(t) is applied to each window  830 C (W 0  . . . W n ), resulting in sets (wW 0  . . . wW n ) of 2048 windowed samples  840 C, e.g., (wW x (0) . . . wW x (2047)). The modified discrete cosine transformation (MDCT) is applied to each set (wW 0  . . . wW n ) of windowed samples  840 C (wW x (0) . . . wW x (2047)), resulting sets (MDCT 0  . . . MDCT n ) of 1024 frequency coefficients  850 C, e.g., (MDCT x (0). . . MDCT x (1023)).  
         [0064]     The encoder  825 B receives the output of the speech/music classification  866 B apparatus. Based upon the output of the speech/music classification apparatus  866 B, the encoder  825 B can take any number of actions with respect to the MDCT coefficients. For example, where the output indicates that the content associated with the audio signal  810 C is speech, the encoder  825 B can either discard or quantize with fewer bits the MDCT coefficients associated with frequencies outside the range of human speech, i.e., exceeding 4 KHz. Where the output indicates that the content associated with the audio signal  810 C is music, the MPEG  825 B can quantize the MDCT coefficients associated with frequencies outside the range of human speech.  
         [0065]     The sets of frequency coefficients  850 C (MDCT 0  . . . MDCT n ) are then quantized and coded for transmission, forming what is known as an audio elementary stream (AES). The AES can be multiplexed with other AESs. The multiplexed signal, known as the Audio Transport Stream (Audio TS) can then be stored and/or transported for playback on a playback device. The playback device can either be local or remotely located.  
         [0066]     Where the playback device is remotely located, the multiplexed signal is transported over a communication medium, such as the Internet. During playback, the Audio TS is de-multiplexed, resulting in the constituent AES signals. The constituent AES signals are then decoded, resulting in the audio signal.  
         [0067]     Alternatively, the frequency coefficients MDCT 0  . . . MDCT n  may be packetized by the packetization engine of  FIG. 6 . In an audio signal, each frame may comprise frequency coefficients  850 C (MDCT 0  . . . MDCT 1023 ). Sub-frame contents may correspond to a particular range of audio frequencies.  
         [0068]      FIG. 7  is a block diagram illustrating an exemplary audio decoder  900  according to an embodiment of the present invention. Referring now to  FIG. 7 , once the frame synchronization is found and delivered from signal processor  901 , the advanced audio coding (AAC) bit stream  903  is de-multiplexed by a bit stream de-multiplexer  905 . This includes Huffman decoding  916 , scale factor decoding  915 , and decoding of side information used in tools such as mono/stereo  920 , intensity stereo  925 , TNS  930 , and the filter bank  935 .  
         [0069]     The sets of frequency coefficients  850 C (MDCT 0  . . . MDCT n ) are decoded and copied to an output buffer in a sample fashion. After Huffman decoding  916 , an inverse quantizer  940  inverse quantizes each set of frequency coefficients  850 C (MDCT 0  . . . MDCT n ) by a 4/3-power nonlinearity. The scale factors  915  are then used to scale sets of frequency coefficients  850 C (MDCT 0  . . . MDCT n ) by the quantizer step size.  
         [0070]     Additionally, tools including the mono/stereo  920 , prediction  923 , intensity stereo coupling  925 , TNS  930 , and filter bank  935  can apply further functions to the sets of frequency coefficients  850 C (MDCT 0  . . . MDCT n ). The gain control  950  transforms the frequency coefficients  850 C (MDCT 0  . . . MDCT n ) into the time domain signal A(t). The gain control  950  transforms the frequency coefficients  850 C by application of the Inverse MDCT (IMDCT), the inverse window function, window overlap, and window adding. The gain control  950  also looks at the flag  875 B. The flag  875 B is a bit that may be either on or off, i.e., having binary digital value of 1 or zero, respectively. For example, if the bit is on, this indicates that the audio signal is music, and if the bit is off, this indicates that the audio signal is speech, or vice versa.  
         [0071]     If the flag  875 B indicates that the audio signal is music the gain control and may then perform the decoding by performing the Inverse MDCT function. The gain control  950  may also report results directly to the audio processing unit  999  for additional processing, playback, or storage. The gain control  950  is adapted to detect at the receiving/decoding end of the audio transmission whether the audio signal is one of music or speech.  
         [0072]     Another music/speech classifier  966 , such as the systems disclosed in  FIG. 3  or  4 , may be provided at the decoder  900 , so that in the circumstance where the signal has been received at the decoder  900  without being classified as one of speech or music, the signal may then be classified. The signal may also be passed to an audio processing unit  999  for storage, playback, or further analysis, as desired.  
         [0073]     One embodiment of the present invention may be implemented as a board level product, as a single chip, application specific integrated circuit (ASIC), or with varying levels integrated on a single chip with other portions of the system as separate components. The degree of integration of the system will primarily be determined by speed and cost considerations. Because of the sophisticated nature of modern processors, it is possible to utilize a commercially available processor, which may be implemented external to an ASIC implementation of the present system. Alternatively, if the processor is available as an ASIC core or logic block, then the commercially available processor can be implemented as part of an ASIC device with various functions implemented as firmware.  
         [0074]     The foregoing description of the exemplary embodiment of the invention has been presented for the purposes of illustration and description. While the invention has been described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from its scope. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.