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This paper presents a new symbol-based retrieval method on a polyphonic music collection which takes a sequence data of users' performances as a query. We focus on chroma and pitch features to yield a robust retrieval with queries which are generated from different arrangements and which include some mistakes. Conditional random fields (CRFs) are used to enhance simultaneous utilization of chroma and pitch features. This is because CRFs can discriminate the correct sequence from all the other candidate sequences without independence assumptions for features of the inputs. Experimental results show that the use of multiple features based on CRFs leads to a significant improvement of retrieval accuracy and accomplishes robust music retrieval regardless of performance style of queries.

A blind speech separation method with a low computational complexity is proposed. This method consists of a combination of independent component analysis with frequency band selection, and a frame-wise spectral softmask method based on an inter-channel power ratio of tentative separated signals in the frequency domain. The softmask cancels the transfer function between sources and separated signals. A theoretical analysis is given. Performance and effectiveness are evaluated via source separation simulations and a computational estimate, and experimental results show the significantly improved performance of the proposed method. The segmen-al signal-to-noise ratio achieves 7 [dB] and 3 [dB], and the cepstral distortion achieves 1 [dB] and 2.5 [dB], in anechoic and reverberant conditions, respectively. Moreover, there can be a reduction of over 80% in computational complexity compared with unmodified FDICA., EUSIPCO 2011: the 19th European Signal Processing Conference, August 29 - September 2, 2011, Barcelona, Spain