scholarly journals A Robust Cover Song Identification System with Two-Level Similarity Fusion and Post-Processing

2018 ◽  
Vol 8 (8) ◽  
pp. 1383 ◽  
Author(s):  
Mingyu Li ◽  
Ning Chen
Keyword(s):  
Information Retrieval ◽  
State Of The Art ◽  
Identification System ◽  
Post Processing ◽  
Similarity Functions ◽  
Processing Level ◽  
Retrieval Scheme ◽  
The Common ◽  
Cover Song ◽  
Music Information

Similarity measurement plays an important role in various information retrieval tasks. In this paper, a music information retrieval scheme based on two-level similarity fusion and post-processing is proposed. At the similarity fusion level, to take full advantage of the common and complementary properties among different descriptors and different similarity functions, first, the track-by-track similarity graphs generated from the same descriptor but different similarity functions are fused with the similarity network fusion (SNF) technique. Then, the obtained first-level fused similarities based on different descriptors are further fused with the mixture Markov model (MMM) technique. At the post-processing level, diffusion is first performed on the two-level fused similarity graph to utilize the underlying track manifold contained within it. Then, a mutual proximity (MP) algorithm is adopted to refine the diffused similarity scores, which helps to reduce the bad influence caused by the “hubness” phenomenon contained in the scores. The performance of the proposed scheme is tested in the cover song identification (CSI) task on three cover song datasets (Covers80, Covers40, and Second Hand Songs (SHS)). The experimental results demonstrate that the proposed scheme outperforms state-of-the-art CSI schemes based on single similarity or similarity fusion.

scholarly journals Symbolic Melodic Similarity: State of the Art and Future Challenges

2016 ◽  
Vol 40 (2) ◽  
pp. 70-83 ◽  
Author(s):  
Valerio Velardo ◽  
Mauro Vallati ◽  
Steven Jan
Keyword(s):  
Information Retrieval ◽  
Comparative Analysis ◽  
State Of The Art ◽  
Music Information Retrieval ◽  
The State ◽  
Information Retrieval Evaluation ◽  
Future Challenges ◽  
Music Information

Fostered by the introduction of the Music Information Retrieval Evaluation Exchange (MIREX) competition, the number of systems that calculate symbolic melodic similarity has recently increased considerably. To understand the state of the art, we provide a comparative analysis of existing algorithms. The analysis is based on eight criteria that help to characterize the systems, highlighting strengths and weaknesses. We also propose a taxonomy that classifies algorithms based on their approach. Both taxonomy and criteria are fruitfully exploited to provide input for new, forthcoming research in the area.

scholarly journals An Effective Framework for Speech and Music Segregation

2020 ◽  
Vol 17 (4) ◽  
pp. 507-514
Author(s):  
Sidra Sajid ◽  
Ali Javed ◽  
Aun Irtaza
Keyword(s):  
Information Retrieval ◽  
Single Channel ◽  
State Of The Art ◽  
Binary Mask ◽  
Multimedia Information Retrieval ◽  
Time Frequency ◽  
Wide Range ◽  
Ideal Binary Mask ◽  
Music Information ◽  
Singer Identification

Speech and music segregation from a single channel is a challenging task due to background interference and intermingled signals of voice and music channels. It is of immense importance due to its utility in wide range of applications such as music information retrieval, singer identification, lyrics recognition and alignment. This paper presents an effective method for speech and music segregation. Considering the repeating nature of music, we first detect the local repeating structures in the signal using a locally defined window for each segment. After detecting the repeating structure, we extract them and perform separation using a soft time-frequency mask. We apply an ideal binary mask to enhance the speech and music intelligibility. We evaluated the proposed method on the mixtures set at -5 dB, 0 dB, 5 dB from Multimedia Information Retrieval-1000 clips (MIR-1K) dataset. Experimental results demonstrate that the proposed method for speech and music segregation outperforms the existing state-of-the-art methods in terms of Global-Normalized-Signal-to-Distortion Ratio (GNSDR) values

Tools for Music Information Retrieval and Playing

2011 ◽  
pp. 120-145
Author(s):  
Antonello D’Aguanno
Keyword(s):  
Feature Extraction ◽  
Information Retrieval ◽  
Performance Analysis ◽  
State Of The Art ◽  
Automatic Recognition ◽  
Musical Instruments ◽  
Pitch Tracking ◽  
Ieee Standard ◽  
Points Of View ◽  
Music Information

State-of-the-art MIR issues are presented and discussed both from the symbolic and audio points of view. As for the symbolic aspects, different approaches are presented in order to provide an overview of the different available solutions for particular MIR tasks. This section ends with an overview of MX, the IEEE standard XML language specifically designed to support interchange between musical notation, performance, analysis, and retrieval applications. As for the audio level, first we focus on blind tasks like beat and tempo tracking, pitch tracking and automatic recognition of musical instruments. Then we present algorithms that work both on compressed and uncompressed data. We analyze the relationships between MIR and feature extraction presenting examples of possible applications. Finally we focus on automatic music synchronization and we introduce a new audio player that supports the MX logic layer and allows to play both score and audio coherently.

scholarly journals Temporal Pyramid Pooling Convolutional Neural Network for Cover Song Identification

2019 ◽  
Author(s):  
Zhesong Yu ◽  
Xiaoshuo Xu ◽  
Xiaoou Chen ◽  
Deshun Yang
Keyword(s):  
Neural Network ◽  
Time Complexity ◽  
State Of The Art ◽  
Representation Learning ◽  
Training Scheme ◽  
Cover Song ◽  
Music Information ◽  
Cover Songs ◽  
Musical Variations ◽  
Extract Information

Cover song identification is an important problem in the field of Music Information Retrieval. Most existing methods rely on hand-crafted features and sequence alignment methods, and further breakthrough is hard to achieve. In this paper, Convolutional Neural Networks (CNNs) are used for representation learning toward this task. We show that they could be naturally adapted to deal with key transposition in cover songs. Additionally, Temporal Pyramid Pooling is utilized to extract information on different scales and transform songs with different lengths into fixed-dimensional representations. Furthermore, a training scheme is designed to enhance the robustness of our model. Extensive experiments demonstrate that combined with these techniques, our approach is robust against musical variations existing in cover songs and outperforms state-of-the-art methods on several datasets with low time complexity.

scholarly journals Vocal Melody Extraction via HRNet-Based Singing Voice Separation and Encoder-Decoder-Based F0 Estimation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 298
Author(s):  
Yongwei Gao ◽  
Xulong Zhang ◽  
Wei Li
Keyword(s):  
Information Retrieval ◽  
High Resolution ◽  
Fundamental Frequency ◽  
State Of The Art ◽  
Main Difficulty ◽  
Harmonic Structure ◽  
Singing Voice ◽  
Pitch Estimation ◽  
Music Information ◽  
Singing Voice Separation

Vocal melody extraction is an important and challenging task in music information retrieval. One main difficulty is that, most of the time, various instruments and singing voices are mixed according to harmonic structure, making it hard to identify the fundamental frequency (F0) of a singing voice. Therefore, reducing the interference of accompaniment is beneficial to pitch estimation of the singing voice. In this paper, we first adopted a high-resolution network (HRNet) to separate vocals from polyphonic music, then designed an encoder-decoder network to estimate the vocal F0 values. Experiment results demonstrate that the effectiveness of the HRNet-based singing voice separation method in reducing the interference of accompaniment on the extraction of vocal melody, and the proposed vocal melody extraction (VME) system outperforms other state-of-the-art algorithms in most cases.

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