397 research outputs found
Multi-label Ferns for Efficient Recognition of Musical Instruments in Recordings
In this paper we introduce multi-label ferns, and apply this technique for
automatic classification of musical instruments in audio recordings. We compare
the performance of our proposed method to a set of binary random ferns, using
jazz recordings as input data. Our main result is obtaining much faster
classification and higher F-score. We also achieve substantial reduction of the
model size
Extended playing techniques: The next milestone in musical instrument recognition
The expressive variability in producing a musical note conveys information
essential to the modeling of orchestration and style. As such, it plays a
crucial role in computer-assisted browsing of massive digital music corpora.
Yet, although the automatic recognition of a musical instrument from the
recording of a single "ordinary" note is considered a solved problem, automatic
identification of instrumental playing technique (IPT) remains largely
underdeveloped. We benchmark machine listening systems for query-by-example
browsing among 143 extended IPTs for 16 instruments, amounting to 469 triplets
of instrument, mute, and technique. We identify and discuss three necessary
conditions for significantly outperforming the traditional mel-frequency
cepstral coefficient (MFCC) baseline: the addition of second-order scattering
coefficients to account for amplitude modulation, the incorporation of
long-range temporal dependencies, and metric learning using large-margin
nearest neighbors (LMNN) to reduce intra-class variability. Evaluating on the
Studio On Line (SOL) dataset, we obtain a precision at rank 5 of 99.7% for
instrument recognition (baseline at 89.0%) and of 61.0% for IPT recognition
(baseline at 44.5%). We interpret this gain through a qualitative assessment of
practical usability and visualization using nonlinear dimensionality reduction.Comment: 10 pages, 9 figures. The source code to reproduce the experiments of
this paper is made available at:
https://www.github.com/mathieulagrange/dlfm201
Evaluating Ground Truth for ADRess as a Preprocess for Automatic Musical Instrument Identification
Most research in musical instrument identification has focused on labeling isolated samples or solo phrases. A robust instrument identification system capable of dealing with polytimbral recordings of instruments remains a necessity in music information retrieval. Experiments are described which evaluate the ground truth of ADRess as a sound source separation technique used as a preprocess to automatic musical instrument identification. The ground truth experiments are based on a number of basic acoustic features, while using a Gaussian Mixture Model as the classification algorithm. Using all 44 acoustic feature dimensions, successful identification rates are achieved
A Comprehensive Review on Audio based Musical Instrument Recognition: Human-Machine Interaction towards Industry 4.0
Over the last two decades, the application of machine technology has shifted from industrial to residential use. Further, advances in hardware and software sectors have led machine technology to its utmost application, the human-machine interaction, a multimodal communication. Multimodal communication refers to the integration of various modalities of information like speech, image, music, gesture, and facial expressions. Music is the non-verbal type of communication that humans often use to express their minds. Thus, Music Information Retrieval (MIR) has become a booming field of research and has gained a lot of interest from the academic community, music industry, and vast multimedia users. The problem in MIR is accessing and retrieving a specific type of music as demanded from the extensive music data. The most inherent problem in MIR is music classification. The essential MIR tasks are artist identification, genre classification, mood classification, music annotation, and instrument recognition. Among these, instrument recognition is a vital sub-task in MIR for various reasons, including retrieval of music information, sound source separation, and automatic music transcription. In recent past years, many researchers have reported different machine learning techniques for musical instrument recognition and proved some of them to be good ones. This article provides a systematic, comprehensive review of the advanced machine learning techniques used for musical instrument recognition. We have stressed on different audio feature descriptors of common choices of classifier learning used for musical instrument recognition. This review article emphasizes on the recent developments in music classification techniques and discusses a few associated future research problems
Music information retrieval: conceptuel framework, annotation and user behaviour
Understanding music is a process both based on and influenced by the knowledge and experience of the listener. Although content-based music retrieval has been given increasing attention in recent years, much of the research still focuses on bottom-up retrieval techniques. In order to make a music information retrieval system appealing and useful to the user, more effort should be spent on constructing systems that both operate directly on the encoding of the physical energy of music and are flexible with respect to users’ experiences.
This thesis is based on a user-centred approach, taking into account the mutual relationship between music as an acoustic phenomenon and as an expressive phenomenon. The issues it addresses are: the lack of a conceptual framework, the shortage of annotated musical audio databases, the lack of understanding of the behaviour of system users and shortage of user-dependent knowledge with respect to high-level features of music.
In the theoretical part of this thesis, a conceptual framework for content-based music information retrieval is defined. The proposed conceptual framework - the first of its kind - is conceived as a coordinating structure between the automatic description of low-level music content, and the description of high-level content by the system users. A general framework for the manual annotation of musical audio is outlined as well. A new methodology for the manual annotation of musical audio is introduced and tested in case studies. The results from these studies show that manually annotated music files can be of great help in the development of accurate analysis tools for music information retrieval.
Empirical investigation is the foundation on which the aforementioned theoretical framework is built. Two elaborate studies involving different experimental issues are presented. In the first study, elements of signification related to spontaneous user behaviour are clarified. In the second study, a global profile of music information retrieval system users is given and their description of high-level content is discussed. This study has uncovered relationships between the users’ demographical background and their perception of expressive and structural features of music. Such a multi-level approach is exceptional as it included a large sample of the population of real users of interactive music systems. Tests have shown that the findings of this study are representative of the targeted population.
Finally, the multi-purpose material provided by the theoretical background and the results from empirical investigations are put into practice in three music information retrieval applications: a prototype of a user interface based on a taxonomy, an annotated database of experimental findings and a prototype semantic user recommender system.
Results are presented and discussed for all methods used. They show that, if reliably generated, the use of knowledge on users can significantly improve the quality of music content analysis. This thesis demonstrates that an informed knowledge of human approaches to music information retrieval provides valuable insights, which may be of particular assistance in the development of user-friendly, content-based access to digital music collections
Spectral-based Features Ranking for Gamelan Instruments Identification using Filter Techniques
 In this paper, we describe an approach of spectral-based features ranking for Javanese gamelan instruments identification using filter techniques. The model extracted spectral-based features set of the signal using Short Time Fourier Transform (STFT). The rank of the features was determined using the five algorithms; namely ReliefF, Chi-Squared, Information Gain, Gain Ratio, and Symmetric Uncertainty. Then, we tested the ranked features by cross validation using Support Vector Machine (SVM). The experiment showed that Gain Ratio algorithm gave the best result, it yielded accuracy of 98.93%
Feature Extraction for Music Information Retrieval
Copyright c © 2009 Jesper Højvang Jensen, except where otherwise stated
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