Automatic Muscial Instrument Sound Classification

2011 ◽  
pp. 83-88
Author(s):  
Alicja A. Wieczorkowska
Keyword(s):  
Musical Instrument ◽  
Automatic Classification ◽  
Musical Instruments ◽  
Process Information ◽  
Sound Classification ◽  
Audio Files ◽  
Classificatory System

The aim of musical instrument sound classification is to process information from audio files by a classificatory system and accurately identify musical instruments playing the processed sounds. This operation and its results are called automatic classification of musical instrument sounds.

scholarly journals Klasifikasi Alat Musik Tradisional Minangkabau

2019 ◽  
Vol 8 (1) ◽  
pp. 88
Author(s):  
Dian Novita Sari ◽  
Desriyeni Desriyeni
Keyword(s):  
Data Collection ◽  
Musical Instrument ◽  
Musical Instruments ◽  
Close Attention ◽  
Descriptive Research ◽  
Lack Of Information ◽  
Data Compilation ◽  
Main Class ◽  
Decimal Classification

Abstract The writing of this paper aims to (1) find out the classification of Minangkabau traditional musical instruments; (2) knowing the process of classifying Minangkabau traditional musical instruments; (3) knowing the obstacles and efforts to overcome obstacles in the process of classifying Minangkabau traditional musical instruments. Writing this paper using descriptive research methods. Data was collected based on observations and interviews from various sources. Based on the results of the study it can be concluded as follows, first classifying the traditional Minangkabau musical instrument the first step taken is data collection and data compilation. Then classify musical instruments based on the types of musical instruments which are divided into five, namely striking, blowing, striking, picking and pressing musical instruments, but there are only four types of musical instruments in Minangkabau, namely striking, blowing, striking and picking instruments. The types of musical instruments are classified according to the guidelines on DDC (dewey decimal classification). The two processes of classifying traditional musical instruments have several parts, as follows: (1) Dewey's classification; (2) Determine the Main Class; (3) Determine Divisions; (4) Determine the Section. The three obstacles in the process of classifying traditional Minangkabau musical instruments are as follows: (1) lack of information regarding musical instruments in the Minangkabau Region including the area of origin of the musical instruments; (2) the difficulty of grouping musical instruments based on the type of musical instrument, because many names of musical instruments are almost the same. Efforts to overcome obstacles in the process of classifying Minangkabau traditional musical instruments are as follows: (1) conducting the process of collecting all data derived from several literatures and institutions that store traditional Minangkabau musical instruments; (2) pay close attention to musical instruments to be grouped according to the type of music.Keywords: classification; traditional musical instruments; minangkabau

scholarly journals Automatic Classification of Musical Instrument Sounds

2003 ◽  
Vol 32 (1) ◽  
pp. 3-21 ◽  
Author(s):  
Perfecto Herrera-Boyer ◽  
Geoffroy Peeters ◽  
Shlomo Dubnov
Keyword(s):  
Musical Instrument ◽  
Automatic Classification

Automatic Classification of Inspectional Categories: Multivariate Theories of Archaeological Data

1981 ◽  
Vol 46 (2) ◽  
pp. 381-396 ◽  
Author(s):  
Robert A. Benfer ◽  
Alice N. Benfer
Keyword(s):  
Automatic Classification ◽  
Multivariate Methods ◽  
Multivariate Models ◽  
Data Set ◽  
Classification Schemes ◽  
Archaeological Data ◽  
History Of ◽  
Classificatory System

The application of extremely complex multivariate models of classification to subjective inspectional methods of categorization is analyzed in detail, with the widely used Texas system of dart point typology as a case study. The history of the development of the Texas dart point typological system is sketched. An attempt by Gunn and Prewitt (1975) to objectify the classificatory system by multivariate methods is criticized. The techniques applied were too idiosyncratic to the particular data set used to be of predictive value. Discriminant function and multivariate classification analysis are discussed in detail, emphasizing simple geometrical examples by which the major principles may be grasped. Suggestions for improvement are offered for those who wish to follow Gunn and Prewitt in constructing automatic classification schemes.

scholarly journals General sound classification and similarity in MPEG-7

2001 ◽  
Vol 6 (2) ◽  
pp. 153-164 ◽  
Author(s):  
Michael Casey
Keyword(s):  
Markov Models ◽  
Temporal Evolution ◽  
Musical Instruments ◽  
Multimedia Content ◽  
Similarity Metrics ◽  
Learning Framework ◽  
Content Description ◽  
Sound Classification ◽  
Reference Tools

We introduce a system for generalised sound classification and similarity using a machine-learning framework. Applications of the system include automatic classification of environmental sounds, musical instruments, music genre and human speakers. In addition to classification, the system may also be used for computing similarity metrics between a target sound and other sounds in a database. We discuss the use of hidden Markov models for representing the temporal evolution of audio spectra and present results of testing the system on classification and retrieval tasks. The system has been incorporated into the MPEG-7 international standard for multimedia content description and is therefore publicly available in the form of a set of standardised interfaces and software reference tools for developers and researchers.

scholarly journals Classification of Musical Instruments using SVM and KNN

2020 ◽  
Vol 9 (7) ◽  
pp. 1186-1190
Keyword(s):  
Automatic Classification ◽  
High Accuracy ◽  
Musical Instruments ◽  
Acoustic Features ◽  
Accuracy Rate ◽  
Digital World ◽  
Knn Classifier ◽  
Modeling Techniques ◽  
Accuracy Level

Automatic classification of musical instruments is a challenging task. Music data classification has become a very popular research in the digital world. Classification of the musical instruments required a huge manual process. This system classifies the musical instruments from a several acoustic features that includes MFCC, Sonogram and MFCC combined with Sonogram. SVM and kNN are two modeling techniques used to classify the features. In this paper, to simply musical instruments classifications based on its features which are extracted from various instruments using recent algorithms. The proposed work compares the performance of kNN with SVM. Identifying the musical instruments and computing its accuracy is performed with the help of SVM and kNN classifier, using the combination of MFCC and Sonogram with SVM a high accuracy rate of 98% achieve in classifying musical instruments. The system tested sixteen musical instruments to find out the accuracy level using SVM and kNN

scholarly journals Monophonic Musical Instrument Sound Classification Using Impulse Response Modeling

2021 ◽  
Vol 12 (5) ◽  
pp. 1667-1672
Author(s):  
Rutuja S Kothe, Et. al.
Keyword(s):  
Impulse Response ◽  
Research Work ◽  
Musical Instrument ◽  
Research Area ◽  
Musical Instruments ◽  
The Body ◽  
Sound Classification ◽  
Social Applications ◽  
Response Modeling ◽  
Body Response

The field of music has promising commercial and social applications. Hence it has attracted the attention of researchers, engineers, sociologists and health care peoples. Therefore this particular research area has been selected. In this manuscript the monophonic musical  classificationsystem using impulse response of the system is presented. In this research work 19  musical instruments monophonic sounds from 4  families are   classified using WEKA classifier. The impulse response is of all musical instruments and families are  computed in Cepstral Domain. AsImpulse response is used to model the body response of the musical instruments and helps to capture the information.  It is different for different instruments. The features are extracted from impulse response and presented to WEKA Classifier. The  Musical instrument classification for individual instruments and family is verified using impulse response modeling. It is found that the impulse response is different for different instruments. It helps to easily distinguish between instrument to instrument and family to family. For individual instruments, the average classification accuracy has been obtained is 83.23% and 85.55% for family classification.

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