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 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

MARSYAS-0.2

2011 ◽  
pp. 31-49 ◽  
Author(s):  
George Tzanetakis
Keyword(s):  
Feature Extraction ◽  
Information Retrieval ◽  
Music Information Retrieval ◽  
Information Retrieval System ◽  
Audio Processing ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Music Information ◽  
Processing Framework ◽  
Design Challenges

Marsyas, is an open source audio processing framework with specific emphasis on building Music Information Retrieval systems. It has been been under development since 1998 and has been used for a variety of projects in both academia and industry. In this chapter, the software architecture of Marsyas will be described. The goal is to highlight design challenges and solutions that are relevant to any MIR software. Keywords: Information Processing, Music, Information Retrieval, System Design, Evaluation, Fast Fourier Transfer (FFT), Feature Extraction, MFCC

scholarly journals Multipitch estimation using judge-based model

2014 ◽  
Vol 62 (4) ◽  
pp. 751-757
Author(s):  
K. Rychlicki-Kicior ◽  
B. Stasiak
Keyword(s):  
Information Retrieval ◽  
Fundamental Frequency ◽  
Music Information Retrieval ◽  
Real Life ◽  
Musical Instruments ◽  
Music Information ◽  
Test Database

Abstract Multipitch estimation, also known as multiple fundamental frequency (F0) estimation, is an important part of the Music Information Retrieval (MIR) field. Although there have been many different approaches proposed, none of them has ever exceeded the abilities of a trained musician. In this work, an iterative cancellation method is analysed, being applied to three different sound representations - salience spectrum obtained using Constant-Q Transform, cepstrum and enhanced autocorrelation result. Real-life recordings of different musical instruments are used as a database and the parameters of the solution are optimized using a simple yet effective metaheuristic approach - the Luus-Jaakola algorithm. The presented approach results in 85% efficiency on the test database.

scholarly journals An Empirical Prediction Methodology for the Emotional Behaviors with the Impact of Musical Features

2020 ◽  
Vol 9 (6) ◽  
pp. 646-649
Keyword(s):  
Feature Extraction ◽  
Information Retrieval ◽  
Real Situation ◽  
Linguistic Information ◽  
Empirical Prediction ◽  
Current State ◽  
Pragmatic Analysis ◽  
State Of Mind ◽  
Music Information ◽  
The Impact

Music is the combination of melody, linguistic information and singer’s mental realm. As popularity of music increases, the choice of songs also varies according to their mental conditions. The mental conditions reach the supreme bliss to melancholy strain based on the musical notes. Majority mostly prefer songs, which satisfy their current state of mind. Pragmatic analysis in music by computer is a difficult task, as emotion is very complex and it camouflages the real situation. Hence, In this paper , trying to classify the songs based on the features of music which helps to classify the emotion more easily. Music feature extraction is done using Music Information Retrieval (MIR) toolbox. The dataset consists of 100 of Hindi songs of 30 seconds clip and later classify the emotion based on Naïve Bayes classification method using Weka API.

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