Audio Acoustic Features Based Tagging and Comparative Analysis of its Classifications

Goel, Somya; Pangasa, Raghav; Dawn, Suma; Arora, Anuja

doi:10.1109/ic3.2018.8530512

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…They experimented with various activation functions, out of which, 'ReLU' (alpha = 0.33) gave the best classification result with the overall F score of 0.602 on IRMAS training data, which we have used as well. Goel et al 20 showed how we can use musical genres to distribute and manage music datasets to increase the accuracy in finding a music item a person wants to listen to. They presented research for creating an appropriate model for genre recognition in audio files using machine learning classifiers on the IRMAS dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

MIC_FuzzyNET: Fuzzy Integral Based Ensemble for Automatic Classification of Musical Instruments From Audio Signals

et al. 2022

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Music has been an integral part of the history of humankind with theories suggesting it is more antediluvian than speech itself. Music is an ordered succession of tones and harmonies that produce sounds characterised by melody and rhythm. Our paper proposes an ensemble deep learning musical instrument classification (MIC) framework, named as MIC_FuzzyNET model which aims to classify the dominant instruments present in musical clips. Firstly, the musical data is converted to three different spectrograms: Constant Q-Transform, Semitone Spectrogram and Mel Spectrogram, which is then stacked to form 3 channel 2D data. This stacked spectrogram is fed to transfer learning models namely, EfficientNetV2 and ResNet18 which output the preliminary classification scores. A fuzzy rank ensemble model is finally employed that assigns the classifier ranks, on the testing data in order to achieve final enhanced classification scores which reduces error and biases for the constituent CNN architectures. Our proposed framework has been evaluated on the Persian Classical Music Instrument Recognition (PCMIR) dataset and Instrument Recognition in Musical Audio Signals (IRMAS) dataset. It has achieved considerably high accuracy, making our proposed framework a robust MIC model.

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Section: Literature Reviewmentioning

confidence: 99%

MIC_FuzzyNET: Fuzzy Integral Based Ensemble for Automatic Classification of Musical Instruments From Audio Signals

et al. 2022

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“…Training with imbalanced data can sometimes impact the ML model's performance. Hence, we prepare an oversampling method for the minority samples in the training pipeline using SMOTE [57]. Figure 4 presents sample images across three distinct representations for four bat species, illustrating both the similarities and unique frequency band characteristics captured in each.…”

mentioning

confidence: 99%

Bat2Web: A Framework for Real-Time Classification of Bat Species Echolocation Signals Using Audio Sensor Data

Mahbub,

Bhagwagar,

Chand

et al. 2024

Sensors

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Bats play a pivotal role in maintaining ecological balance, and studying their behaviors offers vital insights into environmental health and aids in conservation efforts. Determining the presence of various bat species in an environment is essential for many bat studies. Specialized audio sensors can be used to record bat echolocation calls that can then be used to identify bat species. However, the complexity of bat calls presents a significant challenge, necessitating expert analysis and extensive time for accurate interpretation. Recent advances in neural networks can help identify bat species automatically from their echolocation calls. Such neural networks can be integrated into a complete end-to-end system that leverages recent internet of things (IoT) technologies with long-range, low-powered communication protocols to implement automated acoustical monitoring. This paper presents the design and implementation of such a system that uses a tiny neural network for interpreting sensor data derived from bat echolocation signals. A highly compact convolutional neural network (CNN) model was developed that demonstrated excellent performance in bat species identification, achieving an F1-score of 0.9578 and an accuracy rate of 97.5%. The neural network was deployed, and its performance was evaluated on various alternative edge devices, including the NVIDIA Jetson Nano and Google Coral.

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Automatic Speech Recognition of Continuous Speech Signal of Gujarati Language Using Machine Learning

Pandit

Makwana

Bhatt

2021

Advances in Intelligent Systems and Computing

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Audio Acoustic Features Based Tagging and Comparative Analysis of its Classifications

Cited by 4 publications

References 2 publications

MIC_FuzzyNET: Fuzzy Integral Based Ensemble for Automatic Classification of Musical Instruments From Audio Signals

MIC_FuzzyNET: Fuzzy Integral Based Ensemble for Automatic Classification of Musical Instruments From Audio Signals

Bat2Web: A Framework for Real-Time Classification of Bat Species Echolocation Signals Using Audio Sensor Data

Automatic Speech Recognition of Continuous Speech Signal of Gujarati Language Using Machine Learning

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