Short Time Fourier Transform based music genre classification

Elbir, Ahmet; İlhan, Hamza Osman; Serbes, Görkem; Aydin, Nizamettin

doi:10.1109/ebbt.2018.8391437

Cited by 28 publications

(8 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the first works was that developed in [37], where K-nearest neighbor (KNN) was used. Other works have been based on other computational models, such as Gaussian mixture models (GMMs) [15,16], hidden Markov models [19], linear discriminant analysis [5], support vector machines [10,23], artificial neural networks [22], and convolutional neural networks [7,20,28].…”

Section: Related Workmentioning

confidence: 99%

CONEqNet: convolutional music equalizer network

Iriz¹,

Patricio

Berlanga

et al. 2022

Multimed Tools Appl

View full text Add to dashboard Cite

The process of parametric equalization of musical pieces seeks to highlight their qualities by cutting and/or stimulating certain frequencies. In this work, we present a neural model capable of equalizing a song according to the musical genre that is being played at a given moment. It is normal that (1) the equalization should adapt throughout the song and not always be the same for the whole song; and (2) songs do not always belong to a specific musical genre and may contain touches of different musical genres. The neural model designed in this work, called CONEqNet (convolutional music equalizer network), takes these aspects into account and proposes a neural model capable of adapting to the different changes that occur throughout a song and with the possibility of mixing nuances of different musical genres. For the training of this model, the well-known GTzan dataset, which provides 1,000 fragments of songs of 30 seconds each, divided into 10 genres, was used. The paper will show proofs of concept of the performance of the neural model.

show abstract

Section: Related Workmentioning

confidence: 99%

CONEqNet: convolutional music equalizer network

Iriz¹,

Patricio

Berlanga

et al. 2022

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…Fourier Transform is also widely used in the digital signal processing field especially in audio processing, it also makes difficult problems become very simple to analyze [12]. Fourier Transform is a well-known method that is good for extracting the frequency domain behavior of signals [13]. In medical field, Fourier Transform is also used for classify the heartbeat by obtaining a spectrogram of heart [14].…”

Section: Related Workmentioning

confidence: 99%

Automatic Music Transcription Using Fourier Transform for Monophonic and Polyphonic Audio File

Minor¹,

Kartowisastro²

2022

ISI

View full text Add to dashboard Cite

Musical sheet is an important tool for musicians that enables musicians to communicate with each other and help musicians to learn a composition of a song. Sometimes, musicians face an obstacle when they cannot find the musical sheet to learn a new song or it may require payment to get the sheet. The solution for this problem is to learn the song by figuring out the composition of a song using music transcription. Music Transcription is the process of music information retrieval to produce musical notation. Music Transcription using a computational method often called Automatic Music Transcription by upload the audio file as an input and generate musical sheet. The proposed method is solved using Note Value Detection to separate windows by the detected note values and Fourier Transform to recognize the frequency from each window. This study is evaluating the system using three variables; note value, pitch accuracy, and extra notes. The study shows that note value and pitch detection gives a relatively small percentage error. Meanwhile, extra note detection gives a relatively moderate percentage error in every polyphonic file.

show abstract

“…These measurement values are used to evaluate the output classification performance resulting from the developed method. Accuracy, precision, recall, and F-score values are defined in Equations ( 9), ( 10), (11), and Equation (12).…”

Section: Performance Evaluationmentioning

confidence: 99%

“…With the nature of the multire solution analysis, DWT can provide information on protein sequences more effectively and allow biological signals to be analysed in the frequency domain and time domain ( [9]; [6]). This property is not found in signal processing methods, such as the Fourier transform, which can only study signals in the frequency domain ( [10]; [11]; [8]; [12]). Therefore, the advantages of this DWT method can provide more information than other feature representation methods ( [13]; [14]; [15]; [16]; [17]).…”

Section: Introductionmentioning

confidence: 99%

Untitled

2022

IJMA

View full text Add to dashboard Cite

Features play an important role in representing classes in the hierarchy structure, and using unsuitable features will affect classification performance. The discrete wavelet transform (DWT) approach provides the ability to create the appropriate features to represent data. DWT can produce global and local features using different wavelet families and decomposition levels. These two parameters are essential to obtain a suitable representation for classes in the hierarchy structure. This study proposes using a particle swarm optimisation (PSO) algorithm to select the suitable wavelet family and decomposition level for G-protein coupled receptor (GPCR) hierarchical class representation. The results indicate that the PSO algorithm mostly selects Biorthogonal wavelets and decomposition level 2 to represent GPCR protein. Concerning the performance, the proposed method achieved an accuracy of 97.9%, 85.9%, and 77.5% at the family, subfamily, and subsubfamily levels, respectively.

show abstract

Short Time Fourier Transform based music genre classification

Cited by 28 publications

References 5 publications

CONEqNet: convolutional music equalizer network

CONEqNet: convolutional music equalizer network

Automatic Music Transcription Using Fourier Transform for Monophonic and Polyphonic Audio File

Untitled

Contact Info

Product

Resources

About