Adaptive system for broadcast program identification and reporting

Claim 2

A computer-implemented method for identifying and reporting an audible performance of a musical work recorded in a medium of fixed expression, said method comprising the steps of: (a) training a neural network to generate an output signal corresponding to recognition of a selected retinal sample of said audible performance; (b) receiving an audio signal from a performance broadcast; (c) processing said audio signal to create a series of overlapping retinal samples of said audible performance, wherein each said retinal sample includes a plurality of digital signals representing a spectral and temporal distribution of power of said audio signal; (d) generating said neural network output signal responsive to said series of overlapping retinal samples; (e) comparing each said neural network output signal with a corresponding predetermined threshold signal; and (f) logging a record of the broadcast of said musical work performance responsive to said neural network output signal exceeding said corresponding predetermined threshold signal.

Claim 3

The method of claim 1 wherein said retinal samples include selected portions of said audio signal from which invariant features are removed.

Claim 4

The method of claim 1 wherein said training step (a) is performed with said selected retinal sample in combination with random noise.

Claim 5

A computer-implemented method for identifying an I th one of a plurality S of musical works from a first audio signal A 1s representing a first audible performance of a musical work, wherein for each of said plurality S of musical works a set of R weights (W r ) s is predetermined responsive to a portion P 2s spanning a retinal time interval t R of a second audio signal A 2s representing a second audible performance of said each musical work, wherein 1≦s≦S and 1≦r≦R are non-zero positive integers, said method comprising the steps of: (a) creating a plurality R of retinal signals (S r ) representing a spectral and temporal distribution of power in a portion P 1s of said first audio signal A 1s spanning a retinal time interval t R ; (b) combining said retinal signals (S r ) with each of a plurality S of weight sets (W r ) s to create a plurality S of neural output signals (N s ) each representing a weighted combination of said retinal signals (S r ) ; (c) creating a plurality S of ratio signals (N s /T s ) each corresponding to the ratio of each of said neural output signals (N s ) to a corresponding predetermined alarm threshold signal T s ; (d) if at least one of said ratio signals (N s /T s ) exceeds unity, selecting an I th neural output signal N I =N s corresponding to a greatest said ratio signal N I /T I of said plurality S of ratio signals (N s /T s ), whereby said neural output signal N I represents an identity of said I th musical work, otherwise selecting nothing; (e) making an alarm log record responsive to said selection of said neural output signal N I , said alarm log record including an alarm date, an alarm time, and the identity of said I th musical work; and (f) repeating said steps (a)-(f) responsive to a passage of an updating time interval t U <t R .

Claim 6

A computer-implemented method for identifying an I th one of a plurality S of musical works from a first audio signal A 1s representing a first audible performance of a musical work, wherein for each of said plurality S of musical works a set of R weights (W r ) s is predetermined responsive to a portion P 2s spanning a retinal time interval t R of a second audio signal A 2s representing a second audible performance of said each musical work, wherein 1≦s≦S and 1≦r≦R are non-zero positive integers, said method comprising the steps of: (a) creating a plurality R of retinal signals (S r ) representing a spectral and temporal distribution of power in a portion P 1s of said first audio signal A 1s spanning a retinal time interval t R ; (b) combining said retinal signals (S r ) with each of a plurality S of weight sets (W r ) s to create a plurality S of neural output signals (N s ) each representing a weighted combination of said retinal signals (S r ) ; (c) creating a plurality S of ratio signals (N s /T s ) each corresponding to the ratio of each of said neural output signals (N s ) to a corresponding predetermined alarm threshold signal T s ; (d) if at least one of said ratio signals (N s /T s ) exceeds unity, selecting an I th neural output signal N I =N s corresponding to a greatest said ratio signal N I /T I of said plurality S of ratio signals (N s /T s ), whereby said neural output signal N I represents an identity of said I th musical work, otherwise selecting nothing; (e) making an alarm log record responsive to said selection of said neural output signal N I , said alarm log record including an alarm date, an alarm time, and the identity of said I th musical work; and (f) repeating said steps (a)-(f) responsive to a passage of an updating time interval t U <t R .

Claim 7

The method of claim 4 wherein said creating a plurality of R of retinal signals (Sr) comprises the steps of: creating a plurality of first digital signals representing amplitude samples of said first audio signal portion P 1s ; combining said first digital signals to create a set of second digital signals representing the Fourier Transform of said first digital signals spanning a predetermined frequency region; and accumulating one or more said set of second digital signals to form said plurality R of retinal signals (S r ).

Claim 8

The method of claim 5 wherein said each weight set (w r ) s is determined responsive to said second audio signal portion P 2s in combination with a random noise signal.

Claim 9

The method of claim 6 wherein each of said neural output signals (N s ) is exponentially smoothed by combining said each of said neural output signals (N s ) with a weighted combination of one or more earlier values of neural output signals (N s ) to create a smoothed neural output signal N s .

Claim 10

The method of claim 7 wherein said first audible performance of said each musical work is included in a broadcast program.

Claim 11

The method of claim 4 wherein said first and second audible performances of said each musical work are the same performance recorded in a medium of fixed expression.

Claim 12

The method of claim 9 wherein said first audible performance of said each musical work is included in a broadcast program.

Claim 15

The system of claim 11 wherein said retinal samples include selected portions of said audio signal from which invariant features are removed.

Claim 16

The system of claim 11 wherein said training means further comprises: noise biasing means for adding random noise to said selected retinal sample of said audible performance during said training of said neural network means.