An audio information retrieval model based on Manifold Ranking(MR) is proposed, and ranking results are improved using a Relevance Feedback(RF) algorithm. Timbre components are employed as the model’s main feature. T...An audio information retrieval model based on Manifold Ranking(MR) is proposed, and ranking results are improved using a Relevance Feedback(RF) algorithm. Timbre components are employed as the model’s main feature. To compute timbre similarity, extracting the spectrum features for each frame is necessary; the large set of frames is clustered using a Gaussian Mixture Model(GMM) and expectation maximization. The typical spectra frame from GMM is drawn as data points, and MR assigns each data point a relative ranking score, which is treated as a distance instead of as traditional similarity metrics based on pair-wise distance. Furthermore, the MR algorithm can be easily generalized by adding positive and negative examples from the RF algorithm and improves the final result. Experimental results show that the proposed approach effectively improves the ranking capabilities of existing distance functions.展开更多
Recently,many audio search sites headed by Google have used audio fingerprinting technology to search for the same audio and protect the music copyright using one part of the audio data.However,if there are fingerprin...Recently,many audio search sites headed by Google have used audio fingerprinting technology to search for the same audio and protect the music copyright using one part of the audio data.However,if there are fingerprints per audio file,then the amount of query data for the audio search increases.In this paper,we propose a novel method that can reduce the number of fingerprints while providing a level of performance similar to that of existing methods.The proposed method uses the difference of Gaussians which is often used in feature extraction during image signal processing.In the experiment,we use the proposed method and dynamic time warping and undertake an experimental search for the same audio with a success rate of 90%.The proposed method,therefore,can be used for an effective audio search.展开更多
文摘An audio information retrieval model based on Manifold Ranking(MR) is proposed, and ranking results are improved using a Relevance Feedback(RF) algorithm. Timbre components are employed as the model’s main feature. To compute timbre similarity, extracting the spectrum features for each frame is necessary; the large set of frames is clustered using a Gaussian Mixture Model(GMM) and expectation maximization. The typical spectra frame from GMM is drawn as data points, and MR assigns each data point a relative ranking score, which is treated as a distance instead of as traditional similarity metrics based on pair-wise distance. Furthermore, the MR algorithm can be easily generalized by adding positive and negative examples from the RF algorithm and improves the final result. Experimental results show that the proposed approach effectively improves the ranking capabilities of existing distance functions.
文摘Recently,many audio search sites headed by Google have used audio fingerprinting technology to search for the same audio and protect the music copyright using one part of the audio data.However,if there are fingerprints per audio file,then the amount of query data for the audio search increases.In this paper,we propose a novel method that can reduce the number of fingerprints while providing a level of performance similar to that of existing methods.The proposed method uses the difference of Gaussians which is often used in feature extraction during image signal processing.In the experiment,we use the proposed method and dynamic time warping and undertake an experimental search for the same audio with a success rate of 90%.The proposed method,therefore,can be used for an effective audio search.
