Linxing Xiao

Tsinghua University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Jie Zhou

Tsinghua University

Aibo Tian

The University of Texas at Austin

Chee Keat Fong

Hewlett-Packard (United States)

Tong Zhang

South China University of Technology

Wen Li

China University of Mining and Technology

Tong Zhang

Shandong University

Lie Lu

Dolby (Netherlands)

Frank Seide

Microsoft (United States)

Jie Zhou

Capital Normal University

Li Wen

Nanjing University

Cooperative Institutions

Tsinghua University

South China University of Technology

Southeast University

South China Normal University

Microsoft Research Asia (China)

Microsoft Research (United Kingdom)

Microsoft (United States)

Capital Normal University

Hewlett-Packard (United States)

Ministry of Education of the People's Republic of China

Author Statistics

Papers

Citation

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i-10 index

Research Field

Learning a music similarity measure on automatic annotations with application to playlist generation

IEEE International Conference on Acoustics Speech and Signal Processing (2009)

Linxing Xiao Lie Lu Frank Seide Jie Zhou

This paper presents an approach to learn a better music similarity measure and presents an application to music playlist generation. Different from previous work, in our approach, automatically detected music attributes are used to represent each song. A set of kernels is employed in similarity measure, with each kernel measuring on a subset of music attributes and having a different importance weight. In automatic music playlist generation, a ranking method is presented, which considers multiple seed songs and possible outlier seed. Experiments show the effectiveness of the proposed approach, and the quality of the playlist generated based on automatic annotations is comparable to that based on manual annotations.

Similarity measure

Similarity (geometry)

Music Information Retrieval

Kernel (algebra)

10.1109/icassp.2009.4959976

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Citations (13)

Using chroma histogram to measure the perceptual similarity of music

Linxing Xiao Jie Zhou

Automatic evaluation of perceptual similarity is crucial for music retrieval. However, previous works mainly focused on the similarity of timbre and rhythm but not the musical pattern of a song, such as melody and chord. In this paper, we propose a new feature, chroma histogram, to summarize the musical pattern and use a transposition-invariant matching method to compare two chroma histograms. Experiment results demonstrate the efficiency of this method in measuring the similarity of musical pattern.

Chord (peer-to-peer)

Similarity (geometry)

Timbre

Similarity measure

Music Perception

10.1109/icme.2008.4607685

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Citations (5)

Using Statistic Model to Capture the Association between Timbre and Perceived Tempo.

International Symposium/Conference on Music Information Retrieval (2008)

Linxing Xiao Aibo Tian Wen Li Jie Zhou

The estimation of the perceived tempo is required in many MIR applications. However, automatic tempo estimation itself is still an open problem due to the insufficient understanding of the inherent mechanisms of the tempo perception. Published methods only use the information of rhythm pattern, so they may meet the half /double tempo error problem. To solve this problem, We propose to use statistic model to investigate the association between timbre and tempo and use timbre information to improve the performance of tempo estimation. Experiment results show that this approach performs at least comparably to existing tempo extraction algorithms.

Timbre

Statistic

Association (psychology)

Source

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Citations (14)

Using dtw based unsupervised segmentation to improve the vocal part detection in pop music

Linxing Xiao Jie Zhou Tong Zhang

Vocal part detection, which plays an important role in music information retrieval, is still a tough task so far. Previous works focused on short time features, which cannot capture some essential long term characteristics of singing. In this paper, we propose a Dynamic Time Warping based unsupervised segmentation algorithm to divide a pop song into homogeneous segments, which contain either vocal or pure music sound. This procedure makes it possible to design long term feature or classification schema to improve the accuracy of vocal part detection. We also present a segment level classification schema based on the result of segmentation. It will be shown that the classification accuracy is significantly improved.

Dynamic Time Warping

Schema (genetic algorithms)

Feature (linguistics)

10.1109/icme.2008.4607654

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Citations (2)

Content Based Digital Music Management and Retrieval

Springer eBooks (2009)

Jie Zhou Linxing Xiao

10.1007/978-0-387-89024-1_13

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Citations (0)

Histogram matching for music repetition detection

Aibo Tian Li Wen Linxing Xiao Dong Wang Jie Zhou

Repetition detection is a fundamental issue for music thumbnailing and summarization. In this paper, we propose a new feature, called chroma histogram, which enables us to find out repetitive segments from popular songs accurately and quickly. The feature is robust to tempo variation, because sequential information is removed during the process. The low dimensional feature guarantees a very low computational cost, which is proved by theoretic analysis and experimental evaluation. The objective evaluation results demonstrate that our algorithm outperforms previous approaches in terms of both detecting accuracy and efficiency.

Feature (linguistics)

Repetition (rhetorical device)

Feature Matching

10.1109/icme.2009.5202583

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Citations (5)

Automatic and instant ring tone generation based on music structure analysis

Proceedings of the 30th ACM International Conference on Multimedia (2009)

Tong Zhang Chee Keat Fong Linxing Xiao Jie Zhou

Real tones, which are often excerpts from pop songs, have become popular as ring tones. This paper describes how a ring tone can be produced by analyzing the structure of music and selecting the most appropriate portion of the music. With audio feature analysis and pattern recognition methods, the structure of a song can be estimated by deploying both singing voice detection and repetition detection. Then, one or more ring tones can be automatically selected from the song according to heuristic rules. The entire process takes only a few seconds. It is greatly superior in efficiency and ease-of-use than currently available ring tone generation approaches, and can be used in handheld devices, desktop or laptop PCs and web services. Moreover, this unique music structure analysis technology we developed may be used in many other applications as well, such as for browsing, searching and shopping digital music.

Laptop

Tone (literature)

Feature (linguistics)

10.1145/1631272.1631364

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Citations (0)