Archiving the Sonic Ephemeral: Towards a classification of sound installation documentations through spatial audio features

Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was successfully used as a feature to distinguish different heart sounds. In this paper, new entropy was introduced to analyze heart sounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown. The entropy was previously introduced to analyze mammograms. Four common murmurs were considered including aortic regurgitation, mitral regurgitation, aortic stenosis, and mitral stenosis. Wavelet packet transform was employed for heart sound analysis, and the entropy was calculated for deriving feature vectors. Five types of classification were performed to evaluate the discriminatory power of the generated features. The best results were achieved by BayesNet with 96.94% accuracy. The promising results substantiate the effectiveness of the proposed wavelet packet entropy for heart sounds classification.

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Deep Learning Frameworks Applied For Audio-Visual Scene Classification

10.31219/osf.io/6hxrq ◽

2021 ◽

Author(s):

Lam Pham ◽

Alexander Schindler ◽

Mina Schutz ◽

Jasmin Lampert ◽

Sven Schlarb ◽

...

Keyword(s):

Deep Learning ◽

Classification Accuracy ◽

Visual Input ◽

Visual Scene ◽

Scene Classification ◽

Audio Features ◽

Learning Frameworks

In this paper, we present deep learning frameworks for audio-visual scene classification (SC) and indicate how individual visual and audio features as well as their combination affect SC performance.Our extensive experiments, which are conducted on DCASE (IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events) Task 1B development dataset, achieve the best classification accuracy of 82.2\%, 91.1\%, and 93.9\% with audio input only, visual input only, and both audio-visual input, respectively.The highest classification accuracy of 93.9\%, obtained from an ensemble of audio-based and visual-based frameworks, shows an improvement of 16.5\% compared with DCASE baseline.

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Music Emotion Classification based on Lyrics-Audio using Corpus based Emotion

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v8i3.pp1720-1730 ◽

2018 ◽

Vol 8 (3) ◽

pp. 1720 ◽

Cited By ~ 6

Author(s):

Fika Hastarita Rachman ◽

Riyanarto Sarno ◽

Chastine Fatichah

Keyword(s):

Audio Signal ◽

Extraction Process ◽

Emotion Classification ◽

Text Documents ◽

Text Data ◽

Text Document ◽

Audio Features ◽

Test Result ◽

F Measure

Music has lyrics and audio. That’s components can be a feature for music emotion classification. Lyric features were extracted from text data and audio features were extracted from audio signal data.In the classification of emotions, emotion corpus is required for lyrical feature extraction. Corpus Based Emotion (CBE) succeed to increase the value of F-Measure for emotion classification on text documents. The music document has an unstructured format compared with the article text document. So it requires good preprocessing and conversion process before classification process. We used MIREX Dataset for this research. Psycholinguistic and stylistic features were used as lyrics features. Psycholinguistic feature was a feature that related to the category of emotion. In this research, CBE used to support the extraction process of psycholinguistic feature. Stylistic features related with usage of unique words in the lyrics, e.g. ‘ooh’, ‘ah’, ‘yeah’, etc. Energy, temporal and spectrum features were extracted for audio features.The best test result for music emotion classification was the application of Random Forest methods for lyrics and audio features. The value of F-measure was 56.8%.

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A CNN-Based Mosquito Classification Using Image Transformation of Wingbeat Features

Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques - Frontiers in Artificial Intelligence and Applications ◽

10.3233/faia200559 ◽

2020 ◽

Author(s):

Jose Alvaro Luna-Gonzalez ◽

Daniel Robles-Camarillo ◽

Mariko Nakano-Miyatake ◽

Humberto Lanz-Mendoza ◽

Hector Perez-Meana

Keyword(s):

Anopheles Arabiensis ◽

World Wide ◽

Color Image ◽

Audio Features ◽

Audio Feature ◽

Representative Species ◽

Audio Feature Extraction ◽

Feature Values ◽

Fully Connected

In this paper, a classification of mosquito’s specie is performed using mosquito wingbeats samples obtained by optical sensor. Six world-wide representative species of mosquitos, which are Aedes aegypti, Aedes albopictus, Anopheles arabiensis, Anopheles gambiae and Culex pipiens, Culex quinquefasciatus, are considered for classification. A total of 60,000 samples are divided equally in each specie mentioned above. In total, 25 audio feature extraction algorithms are applied to extract 39 feature values per sample. Further, each audio feature is transformed to a color image, which shows audio features presenting by different pixel values. We used a fully connected neural networks for audio features and a convolutional neural network (CNN) for image dataset generated from audio features. The CNN-based classifier shows 90.75% accuracy, which outperforms the accuracy of 87.18% obtained by the first classifier using directly audio features.

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An annotated dataset of bioacoustic sensing and features of mosquitoes

Scientific Data ◽

10.1038/s41597-020-00725-6 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Dinarte Vasconcelos ◽

Nuno Jardim Nunes ◽

João Gomes

Keyword(s):

Feature Extraction ◽

Natural History ◽

Yellow Fever ◽

Control Strategies ◽

Flapping Wing ◽

Effective Control ◽

Wing Beat ◽

History Museum ◽

Audio Features

Abstract As vectors of malaria, dengue, zika, and yellow fever, mosquitoes are considered one of the more severe worldwide health hazards. Widespread surveillance of mosquitoes is essential for understanding their complex ecology and behaviour, and also for predicting and formulating effective control strategies against mosquito-borne diseases. One technique involves using bioacoustics to automatically identify different species from their wing-beat sounds during flight. In this dataset, we collect sounds of three species of mosquitoes: Aedes Aegypti, Culex Quinquefasciatus & Pipiens, and Culiseta. These species were collected and reproduced in the laboratory of the Natural History Museum of Funchal, in Portugal, by entomologists trained to recognize and classify mosquitoes. For collecting the samples, we used a microcontroller and a mobile phone. The dataset presents audio samples collected with different sampling rates, where 34 audio features characterize each sound file, making it is possible to observe how mosquito populations vary heterogeneously. This dataset provides the basis for feature extraction and classification of flapping-wing flight sounds that could be used to identify different species.

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Machine Learning Evaluation for Music Genre Classification of Audio Signals

International Journal of Grid and High Performance Computing ◽

10.4018/ijghpc.2020070104 ◽

2020 ◽

Vol 12 (3) ◽

pp. 57-67

Author(s):

Chetna Dabas ◽

Aditya Agarwal ◽

Naman Gupta ◽

Vaibhav Jain ◽

Siddhant Pathak

Keyword(s):

Machine Learning ◽

Audio Signals ◽

Python Language ◽

Audio Features ◽

Genre Classification ◽

Proposed Model ◽

Spectral Coefficients ◽

Music Genre ◽

Music Genre Classification

Music genre classification has its own popularity index in the present times. Machine learning can play an important role in the music streaming task. This research article proposes a machine learning based model for the classification of music genre. The evaluation of the proposed model is carried out while considering different music genres as in blues, metal, pop, country, classical, disco, jazz and hip-hop. Different audio features utilized in this study include MFCC (Mel Frequency Spectral Coefficients), Delta, Delta-Delta and temporal aspects for processing the data. The implementation of the proposed model has been done in the Python language. The results of the proposed model reveal an accuracy SVM accuracy of 95%. The proposed algorithm has been compared with existing algorithms and the proposed algorithm performs better than the existing ones in terms of accuracy.

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A Comparison of Human against Machine-Classification of Spatial Audio Scenes in Binaural Recordings of Music

Applied Sciences ◽

10.3390/app10175956 ◽

2020 ◽

Vol 10 (17) ◽

pp. 5956

Author(s):

Sławomir K. Zieliński ◽

Hyunkook Lee ◽

Paweł Antoniuk ◽

Oskar Dadan

Keyword(s):

Machine Learning ◽

Classification Accuracy ◽

Screening Test ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Spatial Audio ◽

Extreme Gradient Boosting ◽

Music Ensemble

The purpose of this paper is to compare the performance of human listeners against the selected machine learning algorithms in the task of the classification of spatial audio scenes in binaural recordings of music under practical conditions. The three scenes were subject to classification: (1) music ensemble (a group of musical sources) located in the front, (2) music ensemble located at the back, and (3) music ensemble distributed around a listener. In the listening test, undertaken remotely over the Internet, human listeners reached the classification accuracy of 42.5%. For the listeners who passed the post-screening test, the accuracy was greater, approaching 60%. The above classification task was also undertaken automatically using four machine learning algorithms: convolutional neural network, support vector machines, extreme gradient boosting framework, and logistic regression. The machine learning algorithms substantially outperformed human listeners, with the classification accuracy reaching 84%, when tested under the binaural-room-impulse-response (BRIR) matched conditions. However, when the algorithms were tested under the BRIR mismatched scenario, the accuracy obtained by the algorithms was comparable to that exhibited by the listeners who passed the post-screening test, implying that the machine learning algorithms capability to perform in unknown electro-acoustic conditions needs to be further improved.

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