Automatic music genre classification based on musical instrument track separation

Rosner, Aldona; Kostek, Bożena

doi:10.1007/s10844-017-0464-5

Cited by 51 publications

(20 citation statements)

References 44 publications

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“…Sound classification algorithms will be important to any approach that selects features based on instrument type or musical style. A range of methods for music genre classification have shown promise, including ensemble classifiers and methods that implement sound source segregation approaches, such as non-negative matrix factorization ( Silla et al, 2007 ; Pérez-García et al, 2010 ; Rosner and Kostek, 2018 ). Several instrument classification approaches have also shown promise, including advanced methods using deep convolutional neural networks ( Benetos et al, 2006 ; Gomez et al, 2018 ; Solanki and Pandey, 2019 ; Racharla et al, 2020 ).…”

Section: Haptic Signal-processing Approachesmentioning

confidence: 99%

Can Haptic Stimulation Enhance Music Perception in Hearing-Impaired Listeners?

Fletcher

2021

Front. Neurosci.

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Cochlear implants (CIs) have been remarkably successful at restoring hearing in severely-to-profoundly hearing-impaired individuals. However, users often struggle to deconstruct complex auditory scenes with multiple simultaneous sounds, which can result in reduced music enjoyment and impaired speech understanding in background noise. Hearing aid users often have similar issues, though these are typically less acute. Several recent studies have shown that haptic stimulation can enhance CI listening by giving access to sound features that are poorly transmitted through the electrical CI signal. This “electro-haptic stimulation” improves melody recognition and pitch discrimination, as well as speech-in-noise performance and sound localization. The success of this approach suggests it could also enhance auditory perception in hearing-aid users and other hearing-impaired listeners. This review focuses on the use of haptic stimulation to enhance music perception in hearing-impaired listeners. Music is prevalent throughout everyday life, being critical to media such as film and video games, and often being central to events such as weddings and funerals. It represents the biggest challenge for signal processing, as it is typically an extremely complex acoustic signal, containing multiple simultaneous harmonic and inharmonic sounds. Signal-processing approaches developed for enhancing music perception could therefore have significant utility for other key issues faced by hearing-impaired listeners, such as understanding speech in noisy environments. This review first discusses the limits of music perception in hearing-impaired listeners and the limits of the tactile system. It then discusses the evidence around integration of audio and haptic stimulation in the brain. Next, the features, suitability, and success of current haptic devices for enhancing music perception are reviewed, as well as the signal-processing approaches that could be deployed in future haptic devices. Finally, the cutting-edge technologies that could be exploited for enhancing music perception with haptics are discussed. These include the latest micro motor and driver technology, low-power wireless technology, machine learning, big data, and cloud computing. New approaches for enhancing music perception in hearing-impaired listeners could substantially improve quality of life. Furthermore, effective haptic techniques for providing complex sound information could offer a non-invasive, affordable means for enhancing listening more broadly in hearing-impaired individuals.

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Section: Haptic Signal-processing Approachesmentioning

confidence: 99%

Can Haptic Stimulation Enhance Music Perception in Hearing-Impaired Listeners?

Fletcher

2021

Front. Neurosci.

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“…Music genre classification [8][9][10] is an important branch of music information retrieval. Correct music classification is of great significance for improving the efficiency of music information retrieval.…”

Section: Introductionmentioning

confidence: 99%

Music Feature Extraction and Classification Algorithm Based on Deep Learning

Zhang

2021

Scientific Programming

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With the rapid development of information technology and communication, digital music has grown and exploded. Regarding how to quickly and accurately retrieve the music that users want from huge bulk of music repository, music feature extraction and classification are considered as an important part of music information retrieval and have become a research hotspot in recent years. Traditional music classification approaches use a large number of artificially designed acoustic features. The design of features requires knowledge and in-depth understanding in the domain of music. The features of different classification tasks are often not universal and comprehensive. The existing approach has two shortcomings as follows: ensuring the validity and accuracy of features by manually extracting features and the traditional machine learning classification approaches not performing well on multiclassification problems and not having the ability to be trained on large-scale data. Therefore, this paper converts the audio signal of music into a sound spectrum as a unified representation, avoiding the problem of manual feature selection. According to the characteristics of the sound spectrum, the research has combined 1D convolution, gating mechanism, residual connection, and attention mechanism and proposed a music feature extraction and classification model based on convolutional neural network, which can extract more relevant sound spectrum characteristics of the music category. Finally, this paper designs comparison and ablation experiments. The experimental results show that this approach is better than traditional manual models and machine learning-based approaches.

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“…There are also algorithms which will analyze its harmonic structure [1], [2]. It is also worth noting that sometimes the arrangement or instrumentation plays an important role [3].…”

Section: Introductionmentioning

confidence: 99%