Method of and System For Classification of an Audio Signal

• US 20080243512A1
• Filed: 04/21/2005
• Published: 10/02/2008
• Est. Priority Date: 04/29/2004
• Status: Abandoned Application

• Current Assignee: Koninklijke Philips N.V.
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Breebaart, Dirk Jeroen, Mckinney, Martin
• Application Number:
  US11/568,278
• Publication Number:
  US 20080243512A1
• Time in Patent Office: Days
• US Class Current: 704/270
• CPC Class Codes: G10H 1/0008 Associated control or indicating means
• CPC Class Codes: G10H 2210/031 Musical analysis, i.e. isolation, extraction or identification of musical elements or musical parameters from a raw acoustic signal or from an encoded audio signal neural networks for electrophonic musical instruments or musical processing G10H2250/311
• CPC Class Codes: G10H 2210/076 for extraction of timing, tempo; Beat detection tempo display G10H2220/081; tempo control G10H2210/375
• CPC Class Codes: G10H 2230/015 PDA [personal digital assistant] or palmtop computing devices used for musical purposes, e.g. portable music players, tablet computers, e-readers or smart phones in which mobile telephony functions need not be used touch-screen interfaces G10H2220/096
• CPC Class Codes: G10H 2230/021 Mobile ringtone, i.e. generation, transmission, conversion or downloading of ringing tones or other sounds for mobile telephony; Special musical data formats or protocols herefor mobile telephone transmission specifically adapted for electrophonic musical tools or instruments G10H2240/251
• CPC Class Codes: G10H 2240/061 MP3, i.e. MPEG-1 or MPEG-2 Audio Layer III, lossy audio compression
• CPC Class Codes: G10H 2240/081 Genre classification, i.e. descriptive metadata for classification or selection of musical pieces according to style analysis genre G10H2210/036
• CPC Class Codes: G10H 2240/091 Info, i.e. juxtaposition of unrelated auxiliary information or commercial messages with or between music files metadata G10H2240/075
• CPC Class Codes: G10H 2240/135 Library retrieval index, i.e. using an indexing scheme to efficiently retrieve a music piece
• CPC Class Codes: G10H 2240/155 Library update, i.e. making or modifying a musical database using musical parameters as indices data structures involving musical parameters G10H2240/005
• CPC Class Codes: G10H 2250/031 Spectrum envelope processing
• CPC Class Codes: G10L 15/14 using statistical models, e.g. Hidden Markov Models [HMMs] G10L15/18 takes precedence
• CPC Class Codes: G10L 25/48 specially adapted for particular use

Abstract

The invention describes a method of classifying an audio input signal (1), said method comprising the steps of extracting a number of features (2) of the audio input signal (1), deriving a feature vector (3) for the input audio signal (1) based on these features (2), and determining the probability that the feature vector (3) for the input audio signal (1) falls within any of a number of classes (C1, C2, . . . , Cn), each corresponding to a particular release-date information.

Claims

1. A method of classifying an audio input signal (1), said method comprising:

extracting at least one feature of the audio input signal;

deriving a feature vector for the input audio signal based on the at least one extracted feature; and

determining the probability that the feature vector for the input audio signal falls within any of a number of classes, each corresponding to a particular release-date information.

2. The method according to claim 1, wherein a class representing a particular release-date information is defined based on feature vectors previously calculated for audio signals from an audio signal collection and associated with this release-date information.

3. The method according to claim 2, wherein a class representing a particular release-date information is described by a model derived from a collection of previously calculated feature vectors associated with this release-date information.

4. The method according to claim 1 wherein the determination of the probability of the feature vector for the audio input signal falling within a particular class comprises performing discriminant analysis on the feature vector.

5. The method according to claim 1, wherein the feature vector comprises at least one of:

psycho-acoustic features of the audio input signal and

features describing an auditory model representation of a temporal envelope of the audio input signal.

6. The method according to claim 1, wherein the extraction of a feature comprises calculating a power spectrum for each feature, normalizing the power spectrum, and calculating the energy over a number of distinct energy bands.

7. The method according to claim 1 wherein the features of the input audio signal are adjusted iteratively during the classification process to obtain a feature vector for optimal classification of the input audio signal.

8. A classifying system for classifying an audio input signal, comprising:

a feature extraction unit for extracting at least one feature of the audio input signal;

a derivation unit for deriving a feature vector for the input audio signal based on the at least one extracted feature; and

a probability determination unit for determining the probability that the feature vector for the input audio signal falls within any of a number of classes, each corresponding to a particular release-date information.

9. The classifying system according to claim 8, wherein said system includes an audio processing device for choosing an audio item according to a particular release-date.

10. The classifying system according to claim 8, said audio processing device comprising an automatic DJ apparatus for choosing pieces of music from a music database according to a user-defined sequence of release-date information so that a grouping of the music according to actual or perceived release-date is achieved.

11. A computer program embedded in a computer readable medium, when executed, for performing actions comprising:

extracting at least one feature of the audio input signal;

deriving a feature vector for the input audio signal based on the at least one extracted feature; and

determining the probability that the feature vector for the input audio signal falls within any of a number of classes, each corresponding to a particular release-date information.