Urban Sound Classification
نویسندگان
چکیده
There are many sounds all around us and our brain can easily clearly identify them. Furthermore, processes the received sound signals continuously provides with relevant environmental knowledge. Although not up to level of accuracy brain, there some smart devices which extract necessary information from an audio signal help different algorithms. Over years, various models like Convolutional Neural Networks (CNNs), Artificial (ANNs), Region- (R-CNNs), numerous machine learning techniques have been employed for classification. These methods shown impressive results in distinguishing spectra-temporal patterns categories. The novelty research lies showing that long-short term memory (LSTM) shows a better result classification compared CNN features used. Additionally, we've evaluated model using such as data augmentation feature stacking. With RNN model, we achieved remarkable 87%, setting new benchmark performance on UrbanSound8k dataset. Our findings only advance field but also underscore potential LSTM networks importance innovative stacking improving recognition systems. Key Words: Sound Classification, Urbansound8k, Librosa, Spectrograms, deep learning, CNN, LSTM.
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ژورنال
عنوان ژورنال: Indian Scientific Journal Of Research In Engineering And Management
سال: 2023
ISSN: ['2582-3930']
DOI: https://doi.org/10.55041/ijsrem25684