Detecting Music Genre Using Extreme Gradient Boosting

Murauer, Benjamin; Specht, Günther

doi:10.1145/3184558.3191822

Cited by 13 publications

(6 citation statements)

References 8 publications

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“…Moreover, we reviewed and accepted two papers. In [4], the authors compared the following approaches: (i) ConvNet on spectrograms, and (ii) deep neural net, (iii) ExtraTrees, and (iv) XGBoost on higher-level features extracted by Essentia. They found that 3 https://www.crowdai.org/challenges/www-2018-challenge-learning-to-recognize-musical-genre 4 https://github.com/crowdAI/crowdai-musical-genre-recognition-starter-kit 5 Code and data available at https://github.com/mdeff/fma..…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…3 Moreover, we developed a starter kit with code to handle the data and make a submission. 4 It also featured some examples and a baseline. Finally, participants were encouraged to review the FMA paper [1] for a detailed description of the data as well as the GitHub repository for Jupyter notebooks showing how to use the data, explore it, and train baseline models.…”

Section: The Challengementioning

confidence: 99%

See 2 more Smart Citations

Learning to Recognize Musical Genre from Audio

Defferrard

Mohanty

Carroll

et al. 2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: The Challengementioning

confidence: 99%

See 1 more Smart Citation

Learning to Recognize Musical Genre from Audio

Defferrard

Mohanty

Carroll

et al. 2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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show abstract

“…2) Significantly faster processing speed compared to the traditional audio processing approach such as librosa [23]. A flowchart comparing (a): existing (slow) approach [32][33][34][35][36][37][38][39][40] and (b): our proposed (much faster as shown in Figure 11a) neural network-based audio processing framework (nnAudio). Our proposed neural network is highlighted in yellow.…”

Section: A Summary Of Key Advantagesmentioning

confidence: 99%

nnAudio: An on-the-Fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolutional Neural Networks

Cheuk

Anderson²,

Agres

et al. 2020

IEEE Access

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In this paper, we present nnAudio, a new neural network-based audio processing framework with graphics processing unit (GPU) support that leverages 1D convolutional neural networks to perform time domain to frequency domain conversion. It allows on-the-fly spectrogram extraction due to its fast speed, without the need to store any spectrograms on the disk. Moreover, this approach also allows backpropagation on the waveforms-to-spectrograms transformation layer, and hence, the transformation process can be made trainable, further optimizing the waveform-to-spectrogram transformation for the specific task that the neural network is trained on. All spectrogram implementations scale as Big-O of linear time with respect to the input length. nnAudio, however, leverages the compute unified device architecture (CUDA) of 1D convolutional neural network from PyTorch, its short-time Fourier transform (STFT), Mel spectrogram, and constant-Q transform (CQT) implementations are an order of magnitude faster than other implementations using only the central processing unit (CPU). We tested our framework on three different machines with NVIDIA GPUs, and our framework significantly reduces the spectrogram extraction time from the order of seconds (using a popular python library librosa) to the order of milliseconds, given that the audio recordings are of the same length. When applying nnAudio to variable input audio lengths, an average of 11.5 hours are required to extract 34 spectrogram types with different parameters from the MusicNet dataset using librosa. An average of 2.8 hours is required for nnAudio, which is still four times faster than librosa. Our proposed framework also outperforms existing GPU processing libraries such as Kapre and torchaudio in terms of processing speed.

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“…Benjamin Murauer et.al [4] used extreme gradient boosting classifier to recognize musical genre from Audio on the Web in the Web Conference 2018. They used Convolution Neural Network (CNN) for spectrogram classification, Deep Neural Networks and ensemble methods using various numerical audio features to predict the genre of specified mp3 music files.…”

Section: Related Workmentioning

confidence: 99%

Accurate Liver Disease Prediction with Extreme Gradient Boosting

Murty¹,

Kumar²

2019

IJEAT

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Machine learning is used extensively in medical diagnosis to predict the existence of diseases. Existing classification algorithms are frequently used for automatic detection of diseases. But most of the times, they do not give 100% accurate results. Boosting techniques are often used in Machine learning to get maximum classification accuracy. Though several boosting techniques are in place but the XGBoost algorithm is doing extremely well for some selected data sets. Building an XGBoost model is simple but improving the model by tuning the parameters is a challenging task. There are many parameters to the XGBoost algorithm and deciding what set of parameters to tune and the ideal values of these parameters is a cumbersome and time taking task. We, in this paper, tuned the XGBoost model for the first time for Liver disease prediction and got 99% accuracy by tuning some of the hyper parameters. It is observed that the model proposed by us exhibited highest classification accuracy compared to all other models built till now by machine learning researchers and some regularly used algorithms like Support Vector Machines (SVM), Naive Bayes (NB), C4.5 Decision tree, Random Belief Networks, Alternating Decision Trees (ADT) experimented by us.

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Detecting Music Genre Using Extreme Gradient Boosting

Cited by 13 publications

References 8 publications

Learning to Recognize Musical Genre from Audio

Learning to Recognize Musical Genre from Audio

nnAudio: An on-the-Fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolutional Neural Networks

Accurate Liver Disease Prediction with Extreme Gradient Boosting

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