scholarly journals A Single Predominant Instrument Recognition of Polyphonic Music Using CNN-based Timbre Analysis

2018 ◽  
Vol 7 (3.34) ◽  
pp. 590 ◽  
Author(s):  
Daeyeol Kim ◽  
Tegg Taekyong Sung ◽  
Soo Young Cho ◽  
Gyunghak Lee ◽  
Chae Bong Sohn
Keyword(s):  
Performance Improvement ◽  
Musical Instrument ◽  
Source Analysis ◽  
Backbone Networks ◽  
Genre Classification ◽  
Instrument Recognition ◽  
Music Information ◽  
Short Time ◽  
Mel Frequency Cepstral Coefficient ◽  
Polyphonic Music

Classifying musical instrument from polyphonic music is a challenging but important task in music information retrieval. This work enables to automatically tag music information, such as genre classification. In previous, almost every work of spectrogram analysis has been used Short Time Fourier Transform (STFT) and Mel Frequency Cepstral Coefficient (MFCC). Recently, sparkgram is researched and used in audio source analysis. Moreover, for deep learning approach, modified convolutional neural networks (CNN) widely have been researched, but many results have not been improved drastically. Instead of improving backbone networks, we have researched on preprocessing process.In this paper, we use CNN and Hilbert Spectral Analysis (HSA) to solve the polyphonic music problem. The HSA is performed at the fixed length of polyphonic music, and a predominant instrument is labeled at its result. As result, we have achieved the state-of-the-art result in IRMAS dataset and 3% performance improvement in individual instruments  

scholarly journals A Study : Analysis of Music Features for Musical Instrument Recognition and Music Similarity Search

2013 ◽  
pp. 181-184
Author(s):  
J. Sirisha Devi ◽  
Y. Srinivas ◽  
N. Murali Krishna
Keyword(s):  
Speaker Recognition ◽  
Similarity Search ◽  
Nearest Neighbor ◽  
Musical Instrument ◽  
K Nearest Neighbor ◽  
Mel Frequency Cepstral Coefficients ◽  
Music Similarity ◽  
Instrument Recognition ◽  
Dynamic Time ◽  
Music Information

Lots of work has been done on speech and speaker recognition. Many technologies were developed for the analysis of speech waveforms. Musical instrument recognition is an important aspect of music information retrieval system. In this paper we analyzed features for musical instruments recognition and a brief study on music similarity. Music similarity search is done by using mel-frequency cepstral coefficients (MFCC), linear predictive coefficients (LPC) and the classifier used is K- Nearest Neighbor with Dynamic Time Warping.

scholarly journals Music Genre Classification for Indian Music Genres

2021 ◽  
Vol 9 (8) ◽  
pp. 1756-1762
Author(s):  
Neha Kumari
Keyword(s):  
Feature Extraction ◽  
Contemporary Music ◽  
Speech Analysis ◽  
Machine Learning Techniques ◽  
Genre Classification ◽  
Music Genre ◽  
Indian Music ◽  
Music Information ◽  
Mel Frequency Cepstral Coefficient ◽  
Music Genre Classification

Abstract: Due to the enormous expansion in the accessibility of music data, music genre classification has taken on new significance in recent years. In order to have better access to them, we need to correctly index them. Automatic music genre classification is essential when working with a large collection of music. For the majority of contemporary music genre classification methodologies, researchers have favoured machine learning techniques. In this study, we employed two datasets with different genres. A Deep Learning approach is utilised to train and classify the system. A convolution neural network is used for training and classification. In speech analysis, the most crucial task is to perform speech analysis is feature extraction. The Mel Frequency Cepstral Coefficient (MFCC) is utilised as the main audio feature extraction technique. By extracting the feature vector, the suggested method classifies music into several genres. Our findings suggest that our system has an 80% accuracy level, which will substantially improve on further training and facilitate music genre classification. Keywords: Music Genre Classification, CNN, KNN, Music information retrieval, feature extraction, spectrogram, GTZAN dataset, Indian music genre dataset.

scholarly journals Singing Transcription from Polyphonic Music Using Melody Contour Filtering

2021 ◽  
Vol 11 (13) ◽  
pp. 5913
Author(s):  
Zhuang He ◽  
Yin Feng
Keyword(s):  
Audio Signals ◽  
Continuity Properties ◽  
Information Retrieval Evaluation ◽  
Unique Method ◽  
Image Edge ◽  
Salience Function ◽  
Music Information ◽  
Melody Contour ◽  
Analysis Platform ◽  
Polyphonic Music

Automatic singing transcription and analysis from polyphonic music records are essential in a number of indexing techniques for computational auditory scenes. To obtain a note-level sequence in this work, we divide the singing transcription task into two subtasks: melody extraction and note transcription. We construct a salience function in terms of harmonic and rhythmic similarity and a measurement of spectral balance. Central to our proposed method is the measurement of melody contours, which are calculated using edge searching based on their continuity properties. We calculate the mean contour salience by separating melody analysis from the adjacent breakpoint connective strength matrix, and we select the final melody contour to determine MIDI notes. This unique method, combining audio signals with image edge analysis, provides a more interpretable analysis platform for continuous singing signals. Experimental analysis using Music Information Retrieval Evaluation Exchange (MIREX) datasets shows that our technique achieves promising results both for audio melody extraction and polyphonic singing transcription.

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