A Hierarchical Stratagem for Classification of String Instrument
01 Jan 2020-International Journal of Web-based Learning and Teaching Technologies (IGI Global)-Vol. 15, Iss: 1, pp 1-23
TL;DR: A hierarchical scheme is proposed to classify string instruments without using MFCC-based features, and a neural network, k-NN, Naive Bayes' and Support Vector Machine have been used to classify.
Abstract: Automatic recognition of instrument types from an audio signal is a challenging and a promising research topic. It is challenging as there has been work performed in this domain and because of its applications in the music industry. Different broad categories of instruments like strings, woodwinds, etc., have already been identified. Very few works have been done for the sub-categorization of different categories of instruments. Mel Frequency Cepstral Coefficients (MFCC) is a frequently used acoustic feature. In this work, a hierarchical scheme is proposed to classify string instruments without using MFCC-based features. Chroma reflects the strength of notes in a Western 12-note scale. Chroma-based features are able to differentiate from the different broad categories of string instruments in the first level. The identity of an instrument can be traced through the sound envelope produced by a note which bears a certain pitch. Pitch-based features have been considered to further sub-classify string instruments in the second level. To classify, a neural network, k-NN, Naive Bayes' and Support Vector Machine have been used.
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01 Jan 2018
TL;DR: This chapter conveys the results of an original research study conducted in 2013-2014 to analyze the perceptions of faculty during a learning management system transition (frequent technology change or adoption).
Abstract: This chapter conveys the results of an original research study conducted in 2013-2014 to analyze the perceptions of faculty during a learning management system transition (frequent technology change or adoption). The purpose of the study is to determine if faculty perceptions of adopting new technology have an effect on their stress levels; thereby, affecting faculty preparedness. The literature indicates that higher Technological Self-Efficacy (TSE) should result in lower stress levels. Data analysis reveals faculty who indicated having moderate proficiency of TSE (45%) and possessing moderate stress levels (45%); having somewhat proficiency of TSE (27%) and possessing minor stress levels (32%); and having extreme proficiency of TSE (20%), yet possessing serious stress levels (14%). While these findings differ from other current literature findings, the literature does support the notion that higher stress levels have implications on faculty perceptions, behaviors, and preparedness (Iqbal & Kokash, 2011). Exploration of Faculty’s Perceptions on Technology Change: Implications for Faculty Preparedness to Teach Online Courses
6 citations
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24 Oct 2011
TL;DR: A chroma toolbox is presented, which contains MATLAB implementations for extracting various types of recently proposed pitch-based and chroma-based audio features and discusses two example applications showing that the final music analysis result may crucially depend on the initial feature design step.
Abstract: Chroma-based audio features, which closely correlate to the aspect of harmony, are a well-established tool in processing and analyzing music data. There are many ways of computing and enhancing chroma features, which results in a large number of chroma variants with different properties. In this paper, we present a chroma toolbox [13], which contains MATLAB implementations for extracting various types of recently proposed pitch-based and chroma-based audio features. Providing the MATLAB implementations on a welldocumented website under a GNU-GPL license, our aim is to foster research in music information retrieval. As another goal, we want to raise awareness that there is no single chroma variant that works best in all applications. To this end, we discuss two example applications showing that the final music analysis result may crucially depend on the initial feature design step.
172 citations
01 Apr 2008
TL;DR: There is significant redundancy between and within feature schemes commonly used in practice in practice, suggesting that further feature analysis research is necessary in order to optimize feature selection and achieve better results for the instrument recognition problem.
Abstract: In tackling data mining and pattern recognition tasks, finding a compact but effective set of features has often been found to be a crucial step in the overall problem-solving process. In this paper, we present an empirical study on feature analysis for recognition of classical instrument, using machine learning techniques to select and evaluate features extracted from a number of different feature schemes. It is revealed that there is significant redundancy between and within feature schemes commonly used in practice. Our results suggest that further feature analysis research is necessary in order to optimize feature selection and achieve better results for the instrument recognition problem.
124 citations
TL;DR: In this paper, a set of features is evaluated for recognition of musical instruments out of monophonic musical signals, aiming to achieve a compact representation, the adopted features regard only spectral characteristics of sound and are limited in number.
Abstract: A set of features is evaluated for recognition of musical instruments out of monophonic musical signals. Aiming to achieve a compact representation, the adopted features regard only spectral characteristics of sound and are limited in number. On top of these descriptors, various classification methods are implemented and tested. Over a dataset of 1007 tones from 27 musical instruments, support vectormachines and quadratic discriminant analysis show comparable results with success rates close to 70% of successful classifications. Canonical discriminant analysis never had momentous results, while nearest neighbours performed on average among the employed classifiers. Strings have been the most misclassified instrument family, while very satisfactory results have been obtained with brass and woodwinds. The most relevant features are demonstrated to be the inharmonicity, the spectral centroid, and the energy contained in the first partial.
120 citations
03 Oct 2001
TL;DR: Over a dataset of 1007 tones from 27 musical instruments and without employing any hierarchical structure, quadratic discriminant analysis shows the lowest error rate and outperforming all the other classification methods.
Abstract: A set of features is evaluated for musical instrument recognition out of monophonic musical signals. Aiming to achieve a compact representation, the adopted features regard only spectral characteristics of sound and are limited in number. On top of these descriptors, various classification methods are implemented and tested. Over a dataset of 1007 tones from 27 musical instruments and without employing any hierarchical structure, quadratic discriminant analysis shows the lowest error rate (7.19% for the individual instrument and 3.13% for instrument families), outperforming all the other classification methods (canonical discriminant analysis, support vector machines, nearest neighbours). The most relevant features are demonstrated to be the inharmonicity, the spectral centroid and the energy contained in the first partial.
120 citations
TL;DR: This paper uses training instruments to learn a set of linear manifolds in model parameter space which are then used during transcription to constrain the properties of models fit to the target mixture to supply the system with prior knowledge at different levels of abstraction.
Abstract: This paper presents a general probabilistic model for transcribing single-channel music recordings containing multiple polyphonic instrument sources. The system requires no prior knowledge of the instruments present in the mixture (other than the number), although it can benefit from information about instrument type if available. In contrast to many existing polyphonic transcription systems, our approach explicitly models the individual instruments and is thereby able to assign detected notes to their respective sources. We use training instruments to learn a set of linear manifolds in model parameter space which are then used during transcription to constrain the properties of models fit to the target mixture. This leads to a hierarchical mixture-of-subspaces design which makes it possible to supply the system with prior knowledge at different levels of abstraction. The proposed technique is evaluated on both recorded and synthesized mixtures containing two, three, four, and five instruments each. We compare our approach in terms of transcription with (i.e., detected pitches must be associated with the correct instrument) and without source-assignment to another multi-instrument transcription system as well as a baseline non-negative matrix factorization (NMF) algorithm. For two-instrument mixtures evaluated with source-assignment, we obtain average frame-level F-measures of up to 0.52 in the completely blind transcription setting (i.e., no prior knowledge of the instruments in the mixture) and up to 0.67 if we assume knowledge of the basic instrument types. For transcription without source assignment, these numbers rise to 0.76 and 0.83, respectively.
70 citations