Automatic classification of media entities according to melodic movement properties

Abstract

In connection with a classification system for classifying media entities that merges perceptual classification techniques and digital signal processing classification techniques for improved classification of media entities, a system and methods are provided for automatically classifying and characterizing melodic movement properties of media entities. Such a system and methods may be useful for the indexing of a database or other storage collection of media entities, such as media entities that are audio files, or have portions that are audio files. The methods also help to determine media entities that have similar, or dissimilar as a request may indicate, melodic movement by utilizing classification chain techniques that test distances between media entities in terms of their properties. For example, a neighborhood of songs may be determined within which each song has similar melodic movement properties.

Description

CROSS REFERENCE TO RELATED APPLICATIONS [0001] This application is a continuation of U.S. application Ser. No. 09 / 942,509, filed Aug. 29, 2001, which is hereby incorporated by reference in its entirety.FIELD OF THE INVENTION [0002] The present invention relates to a system and methods for providing automatic classification of media entities according to melodic movement properties. More particularly, the present invention relates to a system and methods for automatically classifying media entities according to perceptual melodic movement properties and melodic movement properties as determined by digital signal processing techniques. BACKGROUND OF THE INVENTION [0003] Classifying information that has subjectively perceived attributes or characteristics is difficult. When the information is one or more musical compositions, classification is complicated by the widely varying subjective perceptions of the musical compositions by different listeners. One listener may perceive a particu...

AI Technical Summary

Benefits of technology

The present invention is a system and methods for automatically classifying and characterizing melodic movement properties of media entities. This can be useful for the indexing of a database or other storage collection of media entities. The methods help to determine media entities that have similar or dissimilar melodic movement by utilizing classification chain techniques that test distances between media entities in terms of their properties. This allows for more accurate classification of media entities based on their melodic movement properties.

Problems solved by technology

Classifying information that has subjectively perceived attributes or characteristics is difficult.

When the information is one or more musical compositions, classification is complicated by the widely varying subjective perceptions of the musical compositions by different listeners.

Composers indicate how to render their musical compositions with annotations such as brightly, softly, etc., but there is no consistent, concise, agreed-upon system for such annotations.

As a result of rapid movement of musical recordings from sheet music to pre-recorded analog media to digital storage and retrieval technologies, this problem has become acute.

However, current classification systems and search and retrieval systems are inadequate for these tasks.

A variety of inadequate classification and search approaches are now used.

This approach has a significant disadvantage in that it involves guessing because the consumer has no familiarity with the musical composition that is selected.

The disadvantage of this approach is that typically the genres are too broad.

However, this approach has a significant disadvantage, namely that the suggested albums or songs are based on extrinsic similarity as indicated by purchase decisions of others, rather than based upon objective similarity of intrinsic attributes of a requested album or song and the suggested albums or songs.

Another disadvantage of collaborative filtering is that output data is normally available only for complete albums and not for individual songs.

Thus, a first album that the consumer likes may be broadly similar to second album, but the second album may contain individual songs that are strikingly dissimilar from the first album, and the consumer has no way to detect or act on such dissimilarity.

Still another disadvantage of collaborative filtering is that it requires a large mass of historical data in order to provide useful search results.

The search results indicating what others bought are only useful after a large number of transactions, so that meaningful patterns and meaningful similarity emerge.

Moreover, early transactions tend to over-influence later buyers, and popular titles tend to self-perpetuate.

A disadvantage of this information is that it may be biased, it may deliberately mischaracterize the recording in the hope of increasing its sales, and it is normally based on inconsistent terms and meanings.

In still another approach, digital signal processing (DSP) analysis is used to try to match characteristics from song to song, but DSP analysis alone has proven to be insufficient for classification purposes.

While DSP analysis may be effective for some groups or classes of songs, it is ineffective for others, and there has so far been no technique for determining what makes the technique effective for some music and not others.

Specifically, such acoustical analysis as has been implemented thus far suffers defects because 1) the effectiveness of the analysis is being questioned regarding the accuracy of the results, thus diminishing the perceived quality by the user and 2) recommendations can only be made if the user manually types in a desired artist or song title, or group of songs from that specific website.

Accordingly, DSP analysis, by itself, is unreliable and thus insufficient for widespread commercial or other use.

Methods, such as those used by the Muscle Fish patent, which use purely signal processing to determine similarities thus have problems.

Another problem with the Muscle Fish approach is that it ignores the observed fact that often times, sounds with similar attributes as calculated by a digital signal processing algorithm will be perceived as sounding very different.

This is because, at present, no previously available digital signal processing approach can match the ability of the human brain for extracting salient information from a stream of data.

As a result, all previous attempts at signal classification using digital signal processing techniques miss important aspects of a signal that the brain uses for determining similarity.

Previous attempts a classification based on connectionist approaches, such as artificial neural networks (ANN), and self organizing feature maps (SOFM) have had only limited success classifying sounds based on similarity.

The amount of computing resources required to train ANN's and SOFM of the required complexity are cost and resource prohibitive.

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