Sound Classification Using Python (original) (raw)
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Automatic Sound Classification Inspired by Auditory Scene Analysis
2000
A sound classification system for the automatic recognition of the acoustic environment in a hearing instrument is dis- cussed. The system distinguishes the four sound classes 'clean speech', 'speech in noise', 'noise', and 'music' and is based on auditory features and hidden Markov models. The em- ployed features describe level fluctuations, the spectral form and harmonicity. Sounds from a large
In this paper, we present the results of research focusing on methods for recognition/classification of audio signals. We consider the results of the research project to serve as a basis for the main module of a hybrid expert system currently under development. In our earlier studies, we conducted research on the effectiveness of three classifiers: fuzzy classifier, neural classifier and WEKA system for reference data. In this project, a particular emphasis was placed on fine-tuning the fuzzy classifier model and on identifying neural classifier applications, taking into account new neural networks that we have not studied so far in connection with sounds classification method
An Expert System for Automatic Classification of Sound Signals
Journal of Telecommunications and Information Technology, 2020
In this paper, we present the results of research focusing on methods for recognition/classification of audio signals. We consider the results of the research project to serve as a basis for the main module of a hybrid expert system currently under development. In our earlier studies, we conducted research on the effectiveness of three classifiers: fuzzy classifier, neural classifier and WEKA system for reference data. In this project, a particular emphasis was placed on fine-tuning the fuzzy classifier model and on identifying neural classifier applications, taking into account new neural networks that we have not studied so far in connection with sounds classification methods
Problems with Automatic Classification of Musical Sounds
Intelligent Information Processing and Web Mining, 2003
Convenient searching of multimedia databases requires well annotated data. Labeling sound data with information like pitch or timbre must be done through sound analysis. In this paper, we deal with the problem of automatic classi cation of musical instrument on the basis of its sound. Although there are algorithms for basic sound descriptors extraction, correct identi cation of instrument still poses a problem. We describe di culties encountered when classifying woodwinds, brass, and strings of contemporary orchestra. We discuss most di cult cases and explain why these sounds cause problems. The conclusions are drawn and presented in brief summary closing the paper.
Hybrid Computerized Method for Environmental Sound Classification
IEEE Access, 2020
Classification of environmental sounds plays a key role in security, investigation, robotics since the study of the sounds present in a specific environment can allow to get significant insights. Lack of standardized methods for an automatic and effective environmental sound classification (ESC) creates a need to be urgently satisfied. As a response to this limitation, in this paper, a hybrid model for automatic and accurate classification of environmental sounds is proposed. Optimum allocation sampling (OAS) is used to elicit the informative samples from each class. The representative samples obtained by OAS are turned into the spectrogram containing their time-frequency-amplitude representation by using a short-time Fourier transform (STFT). The spectrogram is then given as an input to pre-trained AlexNet and Visual Geometry Group (VGG)-16 networks. Multiple deep features are extracted using the pre-trained networks and classified by using multiple classification techniques namely decision tree (fine, medium, coarse kernel), k-nearest neighbor (fine, medium, cosine, cubic, coarse and weighted kernel), support vector machine, linear discriminant analysis, bagged tree and softmax classifiers. The ESC-10, a ten-class environmental sound dataset, is used for the evaluation of the methodology. An accuracy of 90.1%, 95.8%, 94.7%, 87.9%, 95.6%, and 92.4% is obtained with a decision tree, k-neared neighbor, support vector machine, linear discriminant analysis, bagged tree and softmax classifier respectively. The proposed method proved to be robust, effective, and promising in comparison with other existing state-of-the-art techniques, using the same dataset. INDEX TERMS Environmental sound classification, optimal allocation sampling, spectrogram, convolutional neural network, classification techniques.
PERFORMANCE ACCURACY OF CLASSIFICATION ON ENVIRONMENTAL SOUND CLASSIFICATION (ESC_50) DATASET
IJCIRAS, 2020
The classification of audio dataset is intended to distinguish between the different source of audio such as indoor, outdoor and environmental sounds. The environmental sound classification (ESC-50) dataset is composed with a labeled set of 2000 environmental recordings. The spectral centroid method is applied to extract audio features from ESC-50 dataset with waveform audio file (WAV) format. The decision tree is easy to implement and fast for fitting and prediction therefore this proposed system is utilized the coarse tree and medium tree as a classifier. Then fivefold cross-validation is also applied to evaluate the performance of classifier. The proposed system is implemented by using Matlab programming. The classification accuracy of coarse tree is 63.8% whereas the medium tree is 58.6% on ESC-50 dataset.
Applied Sciences
The use of continuous monitoring systems to control aspects such as noise pollution has grown in recent years. The commercial monitoring systems used to date only provide information on noise levels but do not identify the noise sources that generate them. The identification of noise sources is an important aspect in order to apply corrective measures to mitigate the noise levels. In this sense, new technological advances like machine listening can enable the addition of other capabilities to sound monitoring systems such as the detection and classification of noise sources. Despite the increasing development of these systems, researchers have to face some shortcomings. The most frequent ones are on the one hand, the lack of data recorded in real environments and on the other hand, the need for automatic labelling of large volumes of data collected by working monitoring systems. In order to address these needs, in this paper, we present our own sound database recorded in an urban en...
Computational Intelligence in a Classification of Audio Recordings of Nature
Proceedings of the International Conference on Fuzzy Computation Theory and Applications, 2014
This paper presents different ways for a classification of sounds of birds using linguistic approach with a fuzzy system, neural network and WEKA system. Features of sounds of birds species are coded by the selected MPEG-7 descriptors. The models of classification system are based on the audio descriptors for a some chosen species of birds like: Corn Crake, Hawk, Blackbird, Cuckoo, Lesser Whitethroat, Chiffchaff, Eurasian Pygmy Owl, Meadow Pipit, House Sparrow, Firecrest. The paper proposes fuzzy models that definitely bases on the linguistic description. Moreover neural network for classification was proposed. As reference results WEKA system is used.
Studies and Improvements in Automatic Classification of Musical Sound Samples
2003
In this article we shall deal with automatic classification of sound samples and ways to improve the classification results: We describe a classification process which produces high classification success percentage (over 95% for musical instruments) and compare the results of three classification algorithms: Multidimensional Gauss, KNN and LVQ. Next, we introduce several algorithms to improve the sound database self-consistency by removing outliers: LOO, IQR and MIQR. We present our efficient process for Gradual Elimination of Descriptors using Discriminant Analysis (GDE) which improves a previous descriptor selection algorithm . It also enables us to reduce the computation complexity and space requirements of a sound classification process according to specific accuracy needs. Moreover, it allows finding the dominant separating characteristics of the sound samples in a database according to classification taxonomy. The article ends by showing that good classification results do not necessarily mean generalized recognition of the dominant sound source characteristics, but the classifier might actually be focused on the specific attributes of the classified database. By enriching the learning database with diverse samples from other databases we obtain a more general classifier. The dominant descriptors provided by GDE are then more closely related to what is supposed to be the distinctive characteristics of the sound sources. 1