Automatic Sound Classification Research Articles

Sound classification using evolving ensemble models and Particle Swarm Optimization

Automatic sound classification attracts increasing research attention owing to its vast applications, such as robot navigation, environmental sensing, musical instrument classification, medical diagnosis, and surveillance. In this research, we propose an ensemble convolutional bidirectional Long Short-Term Memory (CBiLSTM) network with optimal hyper-parameter selection for undertaking sound classification. We first transform each audio signal into a spectrogram representation using the Short-time Fourier transform (STFT). A Particle Swarm Optimization (PSO) variant is subsequently proposed to optimize the learning rate, weight decay, numbers of filters and hidden units in the convolutional and BiLSTM layers, respectively, in order to extract effective spatial–temporal characteristics from the spectrogram inputs. To tackle the issue of stagnation in optimization, the proposed algorithm incorporates local exploitation using secant and Newton–Raphson methods, promising leader generation using regular and irregular super-ellipse formulae, and three-dimensional spherical search coefficients. Moreover, it takes into account multiple fused elite signals in conjunction with numerical analysis based exploitation to balance between diversification and intensification. A variety of CBiLSTM networks with distinctive optimized settings are devised. An ensemble model is then constructed by incorporating a set of three yielded networks based on a majority voting scheme. Evaluated using several audio data sets, our ensemble CBiLSTM networks outperform those with default and optimal settings identified by other search methods, existing deep architectures and state-of-the-art related studies. In addition to sound classification tasks, the proposed PSO algorithm also outperforms a number of classical and advanced search methods for solving diverse unimodal and multimodal benchmark functions with statistical significance.

An Interpretable Deep Learning Model for Automatic Sound Classification

Deep learning models have improved cutting-edge technologies in many research areas, but their black-box structure makes it difficult to understand their inner workings and the rationale behind their predictions. This may lead to unintended effects, such as being susceptible to adversarial attacks or the reinforcement of biases. There is still a lack of research in the audio domain, despite the increasing interest in developing deep learning models that provide explanations of their decisions. To reduce this gap, we propose a novel interpretable deep learning model for automatic sound classification, which explains its predictions based on the similarity of the input to a set of learned prototypes in a latent space. We leverage domain knowledge by designing a frequency-dependent similarity measure and by considering different time-frequency resolutions in the feature space. The proposed model achieves results that are comparable to that of the state-of-the-art methods in three different sound classification tasks involving speech, music, and environmental audio. In addition, we present two automatic methods to prune the proposed model that exploit its interpretability. Our system is open source and it is accompanied by a web application for the manual editing of the model, which allows for a human-in-the-loop debugging approach.

Sound Classification Using Python

Sound assumes a significant part in human existence. It is one of the fundamental tangible data which we get or see from the climate and their components which have three principal credits viz. Sufficiency (Loudness of the sound), Frequency (The pitch of the sound), Timbre (Quality of the sound or the personality of the sound for example the Sound contrast between a piano and a violin). It is an event generated from the action. Humans are highly efficient to learn and recognize new and various types of sounds and sound events. There is a lot of research work going on Automatic sound classification and it is used in various real-world applications. The paper proposes an examination of an establishment disturbance classifier reliant upon a model affirmation approach using a neural organization. The signs submitted to the neural association are depicted through a lot of 12 MFCC (Mel Frequency Cepstral Coefficient) limits routinely present toward the front finish of an adaptable terminal. The introduction of the classifier, assessed as far as percent misclassification, show an exactness going between 73 % and 95 % relying upon the term of the choice window. Transmitting sound using a machine and expecting an output is considered a highly accurate deep learning task. This technology is used in our smartphones with mobile assistants such as Siri, Alexa, Google Assistant. In the case of the Google Speech recognition data set over 94 percent accuracy is obtained when trying to identify one of 20 words, silence or unknown. It is a very difficult task to recognize audio or sound events systematically and work on it for identification and give output. We are going to work on it using python programming language and some deep learning techniques. It’s a basic model that we are trying to develop, taking the next step to the innovative model that can help society and also which represent the innovative ideas of Engineering Students.

Perceptual Effects of Adjusting Hearing-Aid Gain by Means of a Machine-Learning Approach Based on Individual User Preference.

This study investigated a method to adjust hearing-aid gain by use of a machine-learning algorithm that estimates the optimal setting of gain parameters based on user preference indicated in an iterative paired-comparison procedure. Twenty hearing-impaired participants completed this procedure for 12 different sound scenarios. During the adjustment procedure, their task was to indicate a preference based on one of three sound attributes: Basic Audio Quality, Listening Comfort, or Speech Clarity. In a double-blind comparison of recordings of the processed scenarios, and using the same attributes as criteria, the adjusted gain settings were subsequently compared with two prescribed settings of the same hearing aid (with and without activation of an automatic sound-classification system). The results showed that the adjustment method provided a general improvement of Basic Audio Quality, an improvement of Listening Comfort in a traffic-noise scenario but not in three scenarios with speech babble, and no significant improvement of Speech Clarity. A large variation in gain adjustments was observed across participants, both among those who did benefit and among those who did not benefit from the adjustment. There was no clear connection between the gain adjustments and the perceived benefit, which indicates that the preferred gain settings for a given sound scenario and a given listening intention are highly individual and difficult to predict.

A Spiking Neural Network Framework for Robust Sound Classification.

Environmental sounds form part of our daily life. With the advancement of deep learning models and the abundance of training data, the performance of automatic sound classification (ASC) systems has improved significantly in recent years. However, the high computational cost, hence high power consumption, remains a major hurdle for large-scale implementation of ASC systems on mobile and wearable devices. Motivated by the observations that humans are highly effective and consume little power whilst analyzing complex audio scenes, we propose a biologically plausible ASC framework, namely SOM-SNN. This framework uses the unsupervised self-organizing map (SOM) for representing frequency contents embedded within the acoustic signals, followed by an event-based spiking neural network (SNN) for spatiotemporal spiking pattern classification. We report experimental results on the RWCP environmental sound and TIDIGITS spoken digits datasets, which demonstrate competitive classification accuracies over other deep learning and SNN-based models. The SOM-SNN framework is also shown to be highly robust to corrupting noise after multi-condition training, whereby the model is trained with noise-corrupted sound samples. Moreover, we discover the early decision making capability of the proposed framework: an accurate classification can be made with an only partial presentation of the input.

Voice Detection in Traditionnal Tunisian Music using Audio Features and Supervised Learning Algorithms

The research presented in this paper aims to automatically detect the singing voice in traditional Tunisian music, taking into account the main characteristics of the sound of the voice in this particular music style. This means creating the possibility to automatically identify instrumental and singing sounds. Therefore different methods for the automatic classification of sounds using supervised learning algorithms were compared and evaluated. The research is divided into four successive stages. First, the extraction of features vectors from the audio tracks (through calculation of the parameters of sound perception) followed by the selection and transformation process of relevant features for singing/instrumental discrimination. Then, using learning algorithms, the instrumental and vocal classes were modeled from a manually annotated database. Finally, the evaluation of the decision-making process (indexing) was applied on the test part of the database. The musical databases used for this study consists of extracts from the national sound archives of Centre of Mediterranean and Arabic Music (CMAM) and recordings made especially for this research. The possibility to index audio data (classify/segment) into vocal and instrumental recognition allows for the retrieval of content-based information of musical databases.

Hybrid framework for categorising sounds of Mysticete whales

This study addresses a problem belonging to the domain of whale audio processing, more specifically the automatic classification of sounds produced by the Mysticete species. The specific task is quite challenging given the vast repertoire of the involved species, the adverse acoustic conditions and the nearly inexistent prior scientific work. Two feature sets coming from different domains (frequency and wavelet) were designed to tackle the problem. These are modelled by means of a hybrid technique taking advantage of the merits of a generative and a discriminative classifier. The dataset includes five species ( Blue , Fin , Bowhead , Southern Right , and Humpback ) and it is publicly available at http://www.mobysound.org/. The authors followed a thorough experimental procedure and achieved quite encouraging recognition rates.

Low-complexity F0-based speech/nonspeech discrimination approach for digital hearing aids

Digital hearing aids impose strong complexity and memory constraints on digital signal processing algorithms that implement different applications. This paper proposes a low complexity approach for automatic sound classification in digital hearing aids. The proposed scheme, which operates on a frame-by-frame basis, consists of two stages: analysis stage and classification stage. The analysis stage provides a set of low-complexity signal features derived from fundamental frequency (F0) estimation. Here, F0 estimation is performed by a decimated difference function, which results in a reduced-complexity analysis stage. The classification stage has been designed with the aim of reducing the complexity while maintaining high accuracy rates. Three low-complexity classifiers have been evaluated (tree-based C4.5, 1-Nearest Neighbor (1-NN) and a Multilayer Perceptron (MLP)), the MLP being chosen because it provides the best accuracy rates and fits to the computational and memory constraints of ultra low-power DSP-based hearing aids. The classification stage is composed of a MLP classifier followed by a Hidden Markov Model (HMM), providing a good trade-off solution between complexity and classification accuracy rate. The goal of the proposed approach is to perform a robust discrimination among speech/nonspeech parts of audio signals in commercial digital hearing aids, the computational cost being a critical issue. For the experiments, an audio database including speech, music and noise signals has been used.

Music Neurotechnology for Sound Synthesis: Sound Synthesis with Spiking Neuronal Networks

Music neurotechnology is a new research area emerging at the crossroads of neurobiology, engineering sciences and music. Examples of ongoing research into this new area include the development of brain-computer interfaces to control music systems and systems for automatic classification of sounds informed by the neurobiology of the human auditory apparatus. The authors introduce neurogranular sampling, a new sound synthesis technique based on spiking neuronal networks (SNN). They have implemented a neurogranular sampler using the SNN model developed by Izhikevich, which reproduces the spiking and bursting behavior of known types of cortical neurons. The neurogranular sampler works by taking short segments (or sound grains) from sound files and triggering them when any of the neurons fire.

Two-layer automatic sound classification system for conversation enhancement in hearing aids1

This paper focuses on the development of an automatic sound classifier for digital hearing aids that aims to enhance the listening comprehension when the user goes from a sound environment to another different one. The approach consists in dividing t

Automatic Classification of Musical Instrument Sounds

We present an exhaustive review of research on automatic classification of sounds from musical instruments. Two different but complementary approaches are examined, the perceptual approach and the taxonomic approach. The former is targeted to derive perceptual similarity functions in order to use them for timbre clustering and for searching and retrieving sounds by timbral similarity. The latter is targeted to derive indexes for labeling sounds after culture- or user-biased taxonomies. We review the relevant features that have been used in the two areas and then we present and discuss different techniques for similarity-based clustering of sounds and for classification into pre-defined instrumental categories.