Search

Separating voiced speech from its mixtures with interferences in monaural condition is not only an important but also challenging task. As multipitch tracking can enable much better performance of speech separation for CASA systems, we propose a new multipitch determination algorithm, which can be used under various kinds of noise conditions. In the process of multipitch estimation, a new representation method is utilized together with maximum support constraint and harmonic completeness constraint. The proposed approach can reliably detect up to two pitches in each frame. Sequential grouping is performed based on a new target pitch tracking strategy. System evaluations show that our algorithm leads to significantly better speech separation results than previous ones.

Information and communication is the backbone of modern society. The underlying technology can be organised in four major tasks: processing and generation of information, transfer of information, visualisation of information and storage of information.

The emergence of multimedia data in databases requires adequate methods for information retrieval. In a music data retrieval system by humming, the first stage is to extract exact pitch periods from a flow of signals. Due to the complexity of speech signals, it is difficult to make a robust and practical pitch tracking system. We adopt genetic algorithm in optimizing the control parameters for note segmentation and pitch determination. We applied the results to HumSearch, a commercialized product, as a pitch tracking engine. Experimental results showed that the proposed engine notably improved the performance of the existing engine in HumSearch.

In this chapter, we summarize the research achievements in the area of content-based audio and music retrieval. This chapter covers the research aspects of audio feature extraction, generic audio classification and retrieval, music content analysis, and content-based music retrieval, providing an overview of current research in the area. In addition, two typical systems for content-based audio and music retrieval are discussed in detail. Finally, based on the current technology used in content-based audio/ music retrieval and the demand from real-world applications, future promising directions are identified.

There have been several query-by-humming techniques developed for music retrivel. The techniques either are error-prone due to the inaccuracy of the hummed query or force the users to hum according to a metronome.This paper presesnts a new slope-based query-by-humming technique, in which the retrivel is robust to the inaccuracy in query and the use of metronome is eliminated. A pitch tracking method is developed to construct the melody curve from a user's humming. The method is robust to the noise in the humming. users are even allowed to sing the melody with lyrics. we use melody curve to represent the melodies of the original songs and the hummed query. And curve features like melody slope pitch range, time duration and note changes in the slopes are extrated from the melody curves. A melody slope matching method has been developed to do music retrivel, in which the melody slopes from a query is matched with the melody slopes for the original songs in the database. Results have shown the pitch tracking method and slope-based feature matching algoriyhms are robust to humming inaccuracy.