The Music Retrieval Technology Based On Audio Fingerprint And Version Identifeication

Content-based music retrieval is one hot area of music retrieval. The significantincrease of online music has enhanced its value of application. On the other hand,users’ retrieval demand are also in the change, they are never satisfied with just getthe same song as query, but also want to get multiple versions of music, such asdifferent singers sung the song at different occasions.With the development of thewe-media and the popularity of amateur cover, this demand is becoming more andmore obvious.Content-based music retrieval system will extract feature from the query musicand sample music respectively, and then retrieve the sample which is same as queryby feature matching. The features used in retrieval are often referred to as audiofingerprints, whose format are compact, and tend to match music clips have samecontent. On the contrary, the features used to express music versions are morecomplex, and tend to match music clips sharing the same version features, while thecontent is not necessarily the same. Thus in this paper, the music versions retrievalwill be divided into two processing: audio-fingerprint-based music retrieval andmusic version identification. Music version identification can be done offline in thenormative sample library, while the retrieval based on the audio fingerprint isprocessed in real time, thus for samples which have been retrieved, the versionidentification can provide version-relevant samples immediately(the songs of otherversions for the same music).Due to the outstanding performance of human hearing, I plan to extract featurethat can satisfy the mechanism of acoustic and then construct audio fingerprint fromit. After analyzing the physiological characteristics of the human ear, this paperused the cosine-base and distribution function to simulate the process of the cochleato sounds, and then used the sparse decomposition coefficient as feature. In order toovercome the problem of higher decomposition time, the paper put forward a rapidfeature extraction method based on matching pursuit algorithm.Due to its complex form, the sparse feature based on mechanism of auditorydoes not suitable to sample music retrieval, this paper proposed a series of methodsfor feature quantitative and compression, mainly including the use of MinHash to reduce dimension of the high-dimensional binary sequence feature, using localsensitive hash for rapid retrieval, and then gave the corresponding candidateconfirmation and sample detection method. Experiments showed that the fingerprintfeature has good retrieval efficiency and expressive ability, and has good robustnessto minor noise and global change in time domain, but poor robustness to changes inlocal time domain.In terms of music version identification, this paper analyzed the basic definition,main problems and general processing method of version identification field.Through carding processs for identification and comparative analysis of variousmethods, I constructed a complete music version identification system. In this paper,the commonly used harmonic pitch class profile (HPCP) feature was improved byadding the beat and transposition information. This improved-HPCP is the corefeature of music version identification. This paper also applyed some necessarypreprocessing steps before feature calculation, including peak estimation, beatdetection and reference frequency estimation and so on. Experimental resultsshowed that the builded music version identification system is effective.