BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to multimedia applications, databases and search engines, and more specifically, to a computer-based system and method for automatically generating a summary of a song.

2. Background Information

Governmental, commercial and educational enterprises often utilize database systems to store information. Much of this information is often in text format, and can thus be easily searched for key phrases or for specified search strings. Due to recent advances in both storage capacity and processing power, many database systems now store audio files in addition to the more conventional text-based files. For example, digital juke boxes that can store hundreds, if not thousands, of songs have been developed. A user can select any of these songs for downloading and/or playback. Several commercial entities have also begun selling music, such as CD-ROMs, over the Internet. These entities allow users to search for, select and purchase the CD-ROMs using a browser application, such as Internet Explorer from Microsoft Corp. of Redmond, Wash. or Netscape Navigator from America Online, Inc. of Dulles, Va.

Since there is currently no mechanism for efficiently searching the content of audio files, system administrators typically append conventional, text-based database fields, such as title, author, date, format, keywords, etc. to the audio files. These conventional database fields can then be searched textually by a user to locate specific audio files. For Internet or on-line music systems, the generation of such database fields for each available CD-ROM and/or song can be time-consuming and expensive. It is also subject to data entry and other errors.

For users who do not know the precise title or the artist of the song they are interested in, such text-based search techniques are of limited value. Additionally, a search of database fields for a given search string may identify a number of corresponding songs, even though the user may only be looking for one specific song. In this case, the user may have to listen to substantial portions of the songs to identify the specific song he or she is interested in. Users may also wish to identify the CD-ROM on which a particular song is located. Again, the user may have to listen to significant portions of each song on each CD-ROM in order to locate the particular song that he or she wants. Rather than force users to listen to the first few minutes of each song, a short segment of each song or CD-ROM could be manually extracted and made available to the user for review. The selection of such song segments, however, would be highly subjective and again would be time-consuming and expensive to produce.

Systems have been proposed that allow a user to search audio files by humming or whistling a portion of the song he or she is interested in. These systems process this user input and return the matching song(s). Viable commercial systems employing such melodic query techniques, however, have yet to be demonstrated.

SUMMARY OF THE INVENTION

One aspect of the present invention is the recognition that many songs, especially songs in the rock and popular (“pop”) genres, have specific structures, including repeating phrases or structural elements, such as the chorus or refrain, that are relatively short in duration. These repeating phrases, moreover, are often well known, and can be used to quickly identify specific songs. That is, a user can identify a song just by hearing this repeating phrase or element. Nonetheless, these repeating phrases often do not occur at the beginning of a song. Instead, the first occurrence of such a repeating phrase may not take place for some time, and the most memorable example of the phrase may be its third or fourth occurrence within the song. The present invention relates to a system for analyzing songs and identifying a relatively short, identifiable “key phrase”, such as a repeating phrase that may be used as a summary for the song. This key phrase or summary may then be used as an index to the song.

According to the invention, the song, or a portion thereof, is digitized and converted into a sequence of feature vectors. In the illustrative embodiment, the feature vectors correspond to mel-frequency cepstral coefficients (MFCCs). The feature vectors are then processed in a novel manner in order decipher the song's structure. Those sections that correspond to different structural elements can then be marked with identifying labels. Once the song is labeled, various rules or heuristics are applied to select a key phrase for the song. For example, the system may determine which label appears most frequently within the song, and then select at least some portion of the longest occurrence of that label as the summary.

The deciphering of a song's structure may be accomplished by dividing the song into fixed-length segments, analyzing the feature vectors of the corresponding segments and combining like segments into clusters by applying a distortion algorithm. Alternatively, the system may employ a Hidden Markov Model (HMM) approach in which a specific number of HMM states are selected so as to correspond to the song's labels. After training the HMM, the song is analyzed and an optimization technique is used to determine the most likely HMM state for each frame of the song.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention description below refers to the accompanying drawings, of which:

FIG. 1

is highly schematic block diagram of a computer system for use with the present invention;

FIG. 2

is a highly schematic block diagram of a song summarization system in accordance with the present invention;

FIGS. 3

,

4

and

6

are flow diagrams of preferred methods of the present invention;

FIG. 5

is a partial illustration of a song spectrum that has been analyzed and labeled in accordance with the present invention; and

FIG. 7

is an illustration of a node matrix for use in deciphering a song's structure.

DETAILED DESCRIPTION OF AN ILLUSTRATIVE EMBODIMENT

FIG. 1

shows a computer system

100

having a central processing unit (CPU)

102

that is coupled to a read only memory (ROM)

104

for receiving one or more instruction sets and to a random access memory (RAM)

106

having a plurality of buffers

108

for temporarily storing and retrieving information. A clock

110

is also coupled to the CPU

102

for providing clock or timing signals or pulses thereto. The computer system

100

further includes input/output (I/O) circuitry

112

that interfaces between the CPU

102

and one or more peripheral devices, such as a keyboard

114

, a mouse

116

, a mass memory device

118

(e.g., a hard drive), and a monitor

120

having a display screen (not shown). The computer system

100

may also include a microphone

122

and a speaker

124

that are similarly coupled via I/O circuitry

112

to the CPU

102

. A user may control or interact with the computer system

100

through keyboard

114

and/or mouse

116

. Those skilled in the art will understand that the computer system

100

includes one or more bus structures for interconnecting its various components.

A suitable computer system

100

for use with the present invention includes UNIX workstations, such as those sold by Sun Microsystems, Inc. of Palo Alto, Calif. or Hewlett Packard Company of Palo Alto, Calif., or personal computers sold by Compaq Computer Corporation of Houston, Tex. or International Business Machines Corporation (IBM) of Armonk, N.Y. All of these computers have resident thereon, and are controlled and coordinated by, operating system software, such as UNIX, Microsoft Windows NT or IBM OS2.

FIG. 2

illustrates generally a song summarization system

200

in accordance with the present invention. The system

200

includes a signal processor

202

that is coupled to a feature vector extraction engine

204

. The vector extraction engine

204

is coupled to a labeling engine

206

, which, in turn, is coupled to key phrase identifier logic

208

. An audio input

210

, which is preferably a song or a portion thereof, is provided to the system

200

, as illustrated by arrow

212

. As described herein, the song is then processed by the various components of the system

200

in order to identify a summary or key phrase

214

that is then output by the system

200

as illustrated by arrow

216

. For example, the key phrase

214

may be played back through speaker

124

(

FIG. 1

) and/or stored in RAM

106

or mass memory

118

.

The song summarization system

200

, including its sub-components

202

-

208

, may comprise one or more software programs, such as software modules or libraries, pertaining to the methods described herein, that are resident on a computer readable media, such as mass memory

118

(

FIG. 1

) or RAM

106

, and may be executed by one or more processing elements, such as CPU

102

. Other computer readable media, such as floppy disks and CD-ROMs, may also be used to store the program instructions. Song summarization system

200

and/or one or more of its discrete components

202

-

208

may alternatively be implemented in hardware through a plurality of registers and combinational logic configured to produce sequential logic circuits and cooperating state machines. Those skilled in the art will recognize that various combinations of hardware and software elements may be employed.

Each component

202

-

208

is configured to perform some particular function on the audio input

210

. The signal processor

202

, for example, converts the audio input

210

into a form suitable for processing by the remaining components of the song summarization system

200

. As described above, the audio input

210

is preferably a song, such as a song from the rock or pop genres. The audio input

210

may be received by the system

100

(

FIG. 1

) through microphone

122

. In the preferred embodiment, the audio input

210

is only the first half of the song being summarized. That is, the key phrase is selected from the first half of the song.

The signal processor

102

, among other things, performs an analog-to-digital (A/D) conversion of the song signal (e.g., 16,000 samples per second and 8 bits per sample). This may be performed, for example, by a printed circuit board having a CODEC chip providing 8 bit &mgr;-law compressed samples. Such circuit boards are commercially available from several vendors including Dialogic Corporation of Parsippany, N.J. The signal is then preferably divided or sliced into discrete frames. Each frame may be on the order of 25 milliseconds (ms) in duration and the frames preferably overlap each other by 12.5 ms so that all of the audio input

210

is represented in one or more frames. The signal processor

202

then passes the digitized data corresponding to these frames to the feature vector extraction engine

204

.

It should be understood that the song being summarized may be received by computer

100

(

FIG. 1

) in digitized format and stored at mass memory

118

.

FIG. 3

is a flow diagram illustrating the preferred steps of the present invention. First, engine

204

of the system

200

generates a feature vector for each frame of the audio input

210

, as indicated at step

302

. In the illustrative embodiment, each feature vector is a plurality of mel-frequency cepstral coefficients (MFCCs). A Mel is a psycho-acoustical unit of frequency well known to those skilled in the art. There are several techniques for generating feature vectors comprising MFCCs. For example, the extraction engine

204

may first perform a windowing function, e.g., apply a Hamming window, on each frame. A Hamming window essentially tapers the respective signal to zero at both the beginning and end of the frame, thereby minimizing any discontinuities. Engine

204

may also apply some type of preemphasis on the signal to reduce the amplitude range of the frequency spectrum. In the illustrative embodiment, a preemphasis coefficient of 0.97 is utilized. The time varying data for each frame is then subject to a Fast Fourier Transform function (“FFT”) to obtain a frequency domain signal. The log amplitude of the frequency signal may be warped to the Mel frequency scale and the warped frequency function subject to a second FFT to obtain the parameter set of MFCCs.

More specifically, the frequency domain signal for each frame may be run through a set of triangular filters. In the preferred embodiment, an approximation to the Mel frequency scaling is used. In particular, 40 triangular filters that range between 133 Hz and 6855 Hz are used. The first 13 filters are linearly spaced, while the next 27 are log-spaced. Attached as Appendix A hereto is a description of the triangular filter parameters utilized in the illustrative embodiment. The resulting 40 approximately mel-frequency spaced components for each frame are then subject to a discrete cosine transform (DCT) function to obtain the MFCCs. Preferably, the bottom 13 MFCCs are then passed by the extraction engine

204

to the labeling engine

206

for additional analysis and processing. In other words, the output of the extraction engine

204

is a sequence of vectors, each of n-dimensions (e.g., 13). Each vector, moreover, represents a frame of the audio input

210

. The feature vectors are received at the labeling engine

206

which utilizes the vectors to decipher the song's structure, as indicated at step

304

.

It should be understood that the audio input

210

may be subject to additional processing to reduce the computation power and storage space needed to analyze the respective signal. It should be further understood that other feature vector parameters, besides MFCCs, could be utilized. For example, feature vector extraction engine

204

could be configured to extract spectrum, log spectrum, or autoregressive parameters from the song signal for use in generating the feature vectors.

Clustering Technique

We turn now to

FIG. 4

which is a flow diagram of the preferred method utilized by the labeling engine

206

(

FIG. 2

) to decipher the song's structure. This method may be referred to as the “clustering” technique. First, the feature vectors corresponding to the sequence of frames are organized into segments, as indicated at step

402

. For example, contiguous sequences of feature vectors may be combined into corresponding segments that are each of 1 second duration. Assuming the frames are 25 ms long and overlap each other by 12.5 ms, as described above, there will be approximately 80 feature vectors per segment. Obviously, segments of sizes other than 1 second may be utilized. Next, the feature vectors for each segment are modeled by a Gaussian Distribution, as indicated at step

404

. The mean and covariance of the Gaussian Distribution for each segment is then calculated, as illustrated at block

406

. Suitable methods for modeling vectors by a Gaussian Distribution, and calculating the corresponding mean and covariance parameters is described in J. Deller, J. Hansen, and J. Proakis

Discrete

-

Time Processing of Speech Signals

(IEEE Press Classic Reissue 1993) at pp. 36-41.

It should be understood that the modeling of feature vectors by Gaussian Distributions is effected by calculating the corresponding means and covariances, and thus the step of modeling is just a logical, not an actual, processing step.

For every pair of segments, the labeling engine

206

then computes the distortion between the mean and covariance of the respective Gaussian Distributions, as indicated at block

408

. Distortion, sometimes called distance measure, refers to a measurement of spectral dissimilarity. As explained herein, the distortion between various segments of the song is measured in order to identify those segments that can be considered to be the same and those that are dissimilar. Dissimilar segments are then assigned different labels. A suitable distance measure for computing the distortion between segments is a modified cross-entropy or Kullback-Leibler (KL) distance measure, which is described in M. Siegler et al. “Automatic segmentation, classification and clustering of broadcast news data”

DARPA Speech Recognition Workshop

, pp. 97-99 (1997), which is hereby incorporated by reference in its entirety. The Kullback-Leibler distance measure is modified in order to make it symmetric. More specifically, the labeling engine

206

(

FIG. 2

) uses the equation:

KL

2(

A;B

)=

KL

(

A;B

)+

KL

(

B;A

)  (1)

where, A and B are the two distributions being compared and KL2 is the modified KL distance and

KL

(

A;B

)=

E

[log(

pdf

(

A

))−log(

pdf

(

B

))]  (2)

where pdf stands for the probability density function. Assuming the pdfs for A and B are Gaussian, then equation (1) becomes:

KL

2(

A;B

)=&Sgr;

A/&Sgr;B+&Sgr;B/&Sgr;A

+(&mgr;

A−&mgr;B

)

2

·(1

/&Sgr;A

+1

/&Sgr;B

)  (3)

where &Sgr; denotes variance and &mgr; denotes mean.

Should a given segment lack sufficient data points for the above algorithm, such as when very short segments are used, the Mahalanobis distance algorithm is preferably used. The Mahalanobis distance algorithm can be written as follows for the case when distribution B models too few data points:

M

(

A;B

)=(&mgr;

A−&mgr;B

)

2

/&Sgr;A

Upon computing the distortion for each pair of segments, the labeling engine

206

identifies the two segments having the lowest distortion, as indicated at block

410

, and determines whether this lowest distortion is below a predetermined threshold, as indicated by decision block

412

. In the preferred embodiment, this threshold may range from 0.2 to 1.0 and is preferably on the order of 0.4. Alternatively, the threshold may be based upon the ratio of maximum to minimum distortion for all current segments. If the minimum distortion is less than the threshold, then the feature vectors for the two respective segments are combined as indicated by Yes arrow

414

leading from decision block

412

to block

416

. In other words, the two segments are combined to form a “cluster” whose set of feature vectors is the combination of feature vectors from the two constituent segments.

This combined set of feature vectors is then modeled by a new Gaussian Distribution as indicated at block

418

. Furthermore, the corresponding mean and covariance are calculated for this new Gaussian Distribution, and these values are assigned to the cluster (i.e., the two constituent segments), as indicated at block

420

. As shown by arrow

422

leading from block

420

, processing then returns to step

408

at which point labeling engine

206

computes the distortion between every pair of segments. Since the two segments that were combined to form the cluster have the same mean and covariance, labeling engine

206

only needs to compute the distortion between one of the two constituent segments and the remaining segments. Furthermore, since none of the other mean or covariance values have changed, the only computations that are necessary are the distortions between the newly formed cluster (i.e., one of the two constituent segments) and the remaining segments.

Again, the distortions are examined and the two segments (one of which may now correspond to the previously formed cluster) having the lowest distortion are identified, as indicated by block

410

. Assuming the lowest identified distortion is less than the threshold, then the feature vectors for the two segments (and/or clusters) are combined, as indicated by blocks

412

and

416

. A new Gaussian Distribution model is then generated and new mean and covariance parameters or values are calculated, as indicated by blocks

418

and

420

. The distortion between the remaining segments is then re-computed, again as indicated at block

408

, and the lowest computed distortion is identified and compared to the threshold, as indicated by blocks

410

-

412

. As shown, this process of combining segments (or clusters) whose distortion is below the pre-defined threshold into new clusters is repeated until the distortion between all of the remaining segments and/or clusters is above the threshold.

When the lowest distortion between the current set of clusters and any remaining segments (i.e., those segments that were not combined to form any of the clusters) is above the threshold, then processing is complete as indicated by the No arrow

424

leading from decision block

412

to end block

426

. By identifying those segments of the audio input

210

(e.g., the first half of the song being sumrnmarized) that share similar cepstral features, the system

200

has been able to automatically decipher the song's structure.

It should be recognized that both segments and clusters are simply collections of frames. For segments, which represent the starting point of the clustering technique, the frames are contiguous. For clusters, which represent groups of segments sharing similar cepstral features, the frames are generally not contiguous.

Labeling engine

206

then preferably generates a separate label for each cluster and remaining segments. The labels may simply be a sequence of numbers, e.g., 0, 1, 2, 3, etc., depending on the final number of clusters and remaining segments.

Returning to

FIG. 3

, upon obtaining the labels, the labeling engine

206

preferably uses them to annotate the audio input as indicated at block

306

, preferably on a frame-by-frame basis. More specifically, all of the frames that belong to the same cluster or to the same remaining segment are marked with the label for that cluster or segment, e.g., 0, 1, 2, 3, etc. All of the frames corresponding the same structural elements of the song (i.e., frames having similar cepstral features as determined by the distortion analysis described above) are thus marked with the same label.

FIG. 5

is a partial illustration of a song spectrum

500

that has been analyzed and labeled in accordance with the invention. The song spectrum

500

has been divided into three sections

502

,

504

and

506

because of its length. Associated with each section is an associated label space

508

, which may be located below the song spectrum as shown. The label space

508

contains the labels that have been annotated to the respective song. It may also illustrate the demarcation points or lines separating adjacent labels. For example, a first portion

510

of song segment

502

is annotated with label “0”, while a second portion

512

is annotated with label “1”. A demarcation line

514

shows where, in song segment

502

, the transition from “0” to “1” occurs.

Song segment

504

similarly has a first portion

516

annotated with label “1”. First portion

516

is actually just a continuation of second portion

512

of song segment

502

. A second portion

518

of song segment

504

is annotated with label “2” following demarcation line

520

. Song segment

506

has a first portion

522

, which is simply a continuation of the previous portion

518

, and is thus also annotated with label “2”. Song segment

506

further includes a second portion

524

annotated with label “3” following demarcation line

526

.

After it has been labeled, the audio input is passed to the key phrase identifier logic

208

for additional processing. The key phrase identifier logic

208

preferably examines the labeled audio input and applies some predetermined heuristics to select a key phrase or summary, as indicated by block

308

. For example, the key phrase identifier logic

208

may join consecutive frames having the same label into song portions. Logic

208

then identifies the label that occurs most frequently within the audio input. Logic

208

then selects, as the key phrase for the respective song, the longest duration song portion that is marked with this most frequently occurring label. For example, referring to song spectrum

500

(FIG.

5

), suppose that label “2” is the most frequently occurring label in the song. Furthermore, suppose that the occurrence of label “2” between song segments

504

and

506

(i.e., portions

518

and

522

) is the longest duration of label “2”. If so, at least some part (e.g., the first ten seconds) of this section of the song (i.e., portions

518

and

522

) is preferably selected as the key phrase

214

for the respective song.

The key phrase

214

is then output by the system

200

, as indicated at block

310

. In particular, the key phrase

214

may be played back through speaker

124

. It may also be appended to the respective song file and stored at mass memory

118

. To search specific audio files, a user may simply play-back these automatically generated summaries or key phrases for the audio files stored at mass memory

118

. At this point, processing by system

200

is complete, as indicated by end block

312

.

Those skilled in the art will recognize that the key phrase identifier logic

208

may apply many alternative heuristics or rules to identify the song summary from the labeled frames. For example, instead of choosing the summary from the longest example of the most frequent label, it may be taken from the first occurrence of the most frequent label. Longer or shorter summaries may also be selected.

Hidden Markov Model Technique

Other solutions besides clustering may also be applied to decipher the structure of the song being summarized. For example, the labeling engine

206

may alternatively be configured to use a Hidden Markov Model (HMM) technique to decipher the structure of the respective song so that its various elements may be annotated with labels. Basically, an HMM, having a plurality of states, is established for each song to be summarized. The HMM is preferably ergodic, meaning that the states are fully connected (i.e., any state may be reached directly from any other state, including itself), and all states are emitting.

Each HMM state is intended to correspond to a different structural element of the song that may then be marked with the same label. A series of transition probabilities are also defined for the HMM. The transition probabilities relate to the probability of transitioning between any two HMM states (or transiting back to the same HMM state). In the illustrative embodiment, each HMM state is modeled by a Gaussian Distribution, and is thus characterized by its mean and covariance values. The transition probabilities and Gaussian mean and covariance values are referred to as HMM parameters. Given these parameters, a dynamic programming algorithm is used to find the most likely state sequence through the HMM for the given song, thereby identifying the song's structure. All frames associated with the same HMM state are then assigned the same label. Once the song has been labeled, the heuristics discussed above may be applied to select a key phrase or summary.

FIG. 6

is a flow diagram illustrating the preferred steps for applying the HMM technique to decipher a song's structure. First, as indicated at step

602

, the number of HMM states for use in analyzing the respective song is selected. The number of HMM states for summarizing the first half of a rock or pop song is preferably within the range of 3-15. A preferred number of HMM states is 10. The number of HMM states for each song could be dynamically chosen using some HMM structure learning technique, such as the technique described in M. Brand

Pattern discovery via entropy minimization, Proceedings of Uncertainty

'99 (1999) pp. 12-21.

The parameters of the HMM states are then initialized as indicated at block

604

. For example, all possible state transitions are permitted (i.e., the HMM is ergodic) and all transitions are equally probable. Assuming each HMM state is modeled by a Gaussian Distribution, the mean for each HMM state is set to a randomly assigned number, as indicated at block

606

. The covariance for each HMM state is set to the global covariance, i.e., the covariance calculated using all of the feature vectors of the respective song, as indicated at block

608

. In other words, each feature vector is modeled by a Gaussian Distribution, and the covariance for each modeled feature vector is computed.

Using the Baum-Welch re-estimation algorithm, the HMM parameters (i.e., means, covariances and transition probabilities) are re-estimated, as indicated at block

610

. As a result, the parameter values having the maximum likelihood are determined for each HMM state. The HMM is now trained and can be used to decipher the structure of the song. As shown, the HMN was trained with the feature vectors of the song itself. A suitable description of the Baum-Welch re-estimation algorithm, which is well known to those skilled in the art, can be found in S. Young, D. Kershaw, J. Odell, D. Ollason, V. Valtchev and P. Woodland

The HTK Book

, copr. 1995-1999 Entropic Ltd. (Version 2.2 January 1999) at pp. 8-11, as well as in L. Rabiner and B. Juang

Fundamentals of Speech Recognition

(Prentice Hall 1993) at p. 342.

To decipher the song's structure, labeling engine

206

preferably generates a matrix of HMM states versus frames, as indicated at block

612

.

FIG. 7

is a preferred embodiment of such a matrix, which is designated generally as

700

. As shown, the matrix

700

comprises a plurality of nodes. As described below, each path through the matrix, i.e., the sequence of nodes moving sequentially from frame

1

to the last frame (only 18 frames are shown for clarity), has a corresponding probability. Labeling engine

206

(

FIG. 2

) determines the probability for each path and selects the path with the highest probability, as indicated at step

614

. In the illustrative embodiment, the labeling engine

206

utilizes a dynamic programming algorithm, such as the Viterbi algorithm, to find the path through the matrix

700

having the highest probability. A description of Viterbi decoding or alignment can be found in the

HTK Book

at pp. 11-13, and in

Fundamentals of Speech Recognition

at pp. 339-342.

In particular, as described above, each HMM state has an associated mean and covariance value (i.e., the HMM state's observation values). The feature vector for each frame similarly has an associated mean and covariance value. Thus, a probability can be determined for how closely a given feature vector matches an HMM state. In other words, each frame has a certain probability that it is associated with each HMM state. In addition, there is a probability associated with each transition from one HMM state to another or for staying in the same HMM state. The labeling engine

206

may thus determine the overall probability for each possible path through the matrix

700

starting from the first frame (i.e., frame

1

of

FIG. 7

) and moving sequentially through all the remaining frames.

Movement through the matrix

700

is subject to certain constraints. For example, each path must include a single occurrence of each frame and must proceed sequentially from frame

1

to the last frame. In other words, no frame may be skipped and no frame can be associated with more than one HMM state. Thus, vertical and right-to-left moves are prohibited. The only permissible moves are horizontal and diagonal left-to-right. For purposes of clarity, only three paths

702

,

704

and

706

are illustrated in matrix

700

. As explained above, an overall probability is associated with each path through the matrix, including paths

702

,

704

and

706

. Suppose labeling engine

206

determines that path

704

represents the highest probability path through matrix

700

. By finding the highest probability path through the matrix

700

, labeling engine

206

has effectively deciphered the song's structure. In particular, the song's structure corresponds to the sequence of HMM states along the highest probability path. This completes processing as indicated by end block

616

. Labeling engine

206

then uses the identified HMM states of this path

704

to mark the song with labels, as indicated at step

306

(FIG.

3

). In particular, with reference to

FIG. 7

, engine

206

annotates frames

1

and

2

with label “1”, frames

3

-

6

with label “2”, frames

7

-

10

with label “4”, frames

11

-

15

with label “7”, frames

16

-

18

with label “4”, and so on.

Once the song has been labeled, it is passed to the key phrase identifier logic

208

(FIG.

2

), which applies the desired heuristics for identifying and selecting the key phrase

214

as indicated at step

308

(

FIG. 3

) described above. For example, logic

208

may identify and select the longest duration song portion that is marked with the most frequently occurring label. It should be understood that once the probability for a given path with matrix

700

below a threshold, labeling engine

206

may drop that path from further consideration to conserve processor and memory resources.

The foregoing description has been directed to specific embodiments of this invention. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For example, the methods of the present invention could be applied to more complex musical compositions, such as classical music or opera. Here, the threshold for distinguishing between clusters and the number of HMM states may need to be adjusted. Therefore, it is an object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the invention.

APPENDIX

Filter Number

Low Frequency

Mid Frequency

High Frequency

1

133.333

200.00

266.667

2

200.000

266.667

333.333

3

266.667

333.333

400.000

4

333.333

400.000

466.667

5

400.000

466.667

533.333

6

466.667

533.333

600.000

7

533.333

600.000

666.667

8

600.000

666.667

733.333

9

666.667

733.333

800.000

10

733.333

800.000

866.667

11

800.000

866.667

933.333

12

866.667

933.333

1000.000

13

933.333

1000.000

1071.170

14

1000.000

1071.170

1147.406

15

1071.170

1147.406

1229.067

16

1147.406

1229.067

1316.540

17

1229.067

1316.540

1410.239

18

1316.540

1410.239

1510.606

19

1410.239

1510.606

1618.117

20

1510.606

1618.117

1733.278

21

1618.117

1733.278

1856.636

22

1733.278

1856.636

1988.774

23

1856.636

1988.774

2130.316

24

1988.774

2130.316

2281.931

25

2130.316

2281.931

2444.337

26

2281.931

2444.337

2618.301

27

2444.337

2618.301

2804.646

28

2618.301

2804.646

3004.254

29

2804.646

3004.254

3218.068

30

3004.254

3218.068

3447.099

31

3218.068

3447.099

3692.430

32

3447.099

3692.430

3955.221

33

3692.430

3955.221

4236.716

34

3955.221

4236.716

4538.244

35

4236.716

4538.244

4861.232

36

4538.244

4861.232

5207.208

37

4861.232

5207.208

5577.807

38

5207.208

5577.807

5974.781

39

5577.807

5974.781

6400.008

40

5974.781

6400.008

6855.499