Optimization of Music Retrieval Method Based on Audio Fingerprint
In order to study an efficient music retrieval method,audio fingerprint extraction and parallel retrieval optimization are discussed. Firstly,the audio fingerprint is extracted by spectral clustering method,the spectral feature matrix is constructed by Short-Time Fourier Transform (STFT) and power spectral density calculation,and the audio fingerprint vector is generated by spectral clustering algorithm. Secondly,Apache Lucene is used for parallel search optimization,and the efficiency of search is significantly improved through sharded index and multithreading. Finally,based on Freesound data set,Librosa library was used for feature extraction,and Lucene was used for parallel retrieval experiment. The results show that the music retrieval method based on audio fingerprint is significantly better than the conventional strategy in terms of retrieval time.
music retrievalspectral clusteringparallel optimizationaudio fingerprint
万方数据
维普
