Sound classification neural network. Respiratory S...
Sound classification neural network. Respiratory Sound Classification (RSC) has largely relied on audio-only pipelines trained on small, single-source datasets, which limits out-of-domain generalization. As a result, the accuracy, training time, and prediction time of each model are compared. Further comparisons draw insights that illustrate the need for more finetuning for sound spectrum data when using non-CNN models for sound classification due to the shape of the input In this paper, we proposed a sound classification mechanism based on convolutional neural networks and used the MFCC sound feature extraction method to convert sound signals into spectrograms. Towards Accurate Auscultation Sound Classification with Convolutional Neural Network - Abhishek, S, Mannava, Mahima Chowdary, Ananthapadmanabhan, A. has introduced a novel hybrid quantum-classical Inception neural network model designed for image classification, integrating quantum computing with classical deep learning. Ilipbayeva, N. 16442v1), presents the first hardware implementation of an event-graph neural network for audio classification and keyword spotting, deployed on a system-on-chip FPGA (field-programmable gate array). I. Past quantum neural network research has mostly focused on constructing some kind of variational quantum circuit and attempting to embed it into traditional neural network structures. Moreover, a multi-convolutional neural network (mCNN) is constructed to achieve the classification of normal, aortic stenosis, mitral regurgitation, mitral stenosis, and mitral valve prolapse. This work proposes a fusion-based framework that uses local acoustic scene information from convolutional neural network H. Wang, Heart sound classification based on log Mel-frequency spectral coefficients features and convolutional neural networks, Biomed. Nov 3, 2021 · In order to learn time and frequency features from Log-Mel spectrogram more effectively, a temporal-frequency attention based convolutional neural network model (TFCNN) is proposed in this Oct 9, 2024 · Audio recognition and classification with the help of AI methods has use cases in many different areas. 18, 2026 /PRNewswire/ -- WiMi Hologram Cloud Inc. Pan, R. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, proposed a brand-new technological innovation—a hybrid quantum-classical Inception neural network model for image classification. 1109/ACCESS. , mean, minimum, maximum) and content-based features like bandwidth and frequency. One is for instance voice analysis including voice transcriptions and automatic voice-based emotion analysis to prioritize emergency calls or customer inquiries. Chollet, 2018). Smailov, Deep residual neural network-based classification of loaded and unloaded UAV images, unpublished. Mar 24, 2021 · This article explains how to train a CNN to classify species based on audio information. Our findings underscore the effectiveness of employing graphs to represent audio data. 2888882, IEEE Access Sound Classification using Convolutional Neural Network an d Tensor Deep Stacking Network VOLUME XX, 2017 1 We'll look into audio categorization using deep learning principles like Artificial Neural Networks (ANN), 1D Convolutional Neural Networks (CNN1D), and CNN2D in this repository. Different sources of variation affect real-world acoustic scene (environmental sounds) data, including different recording locations and devices. The models are also tested and evaluated in scenarios where there is a need to distinguish objects between multiple, often similar classes. The recognition of spoken English Alphabets by different people with BEIJING, Feb. CNNs or convolutional neural nets are a type of deep learning algorithm that does really well at learning images. 18, 2026 /PRNewswire/ — WiMi Hologram Cloud Inc. He, Kaiming, et al (2015). Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification, music classification, speech emotion classification and sound event detection. It is shown that pre-trained models on extensive visual and audio datasets can be effectively adapted for respiratory sound classification and to address the class imbalance present in the ICBHI 2017 dataset. Classification of environmental sounds using 1D convolutional Neural network on Urbansound8k dataset This is an implementation of the paper https://arxiv. This new architecture aims to improve performance, efficiency, and robustness by leveraging quantum computing’s feature expression capabilities while maintaining engineering practicality. 10. Recently, neural networks have been applied to tackle audio pattern recognition problems. 16442: Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA Rigdelet neural network and improved partial reinforcement effect optimizer for music genre classification from sound spectrum images (Englisch) Explore the fundamentals of Feedforward Neural Networks, their structure, and applications in classification and regression tasks. Respiratory sounds contain critical diagnostic information for the early identification of severe lung diseases. We undertake some basic data preprocessing and feature extraction on audio sources before developing models. Jan 16, 2024 · We propose a novel method for sound classification, namely NeuProNet, a neural profiling network capable of learning latent profile representation shared between audio samples from identical sources. The data for this example are bird and frog recordings from the Kaggle competition Rainforest Connection Species Audio Detection. ” International Conference on Artificial Intelligence and Statistics. 2010. A predominant observation was that rodents exhibited two primary search strategies: systematic searching and random probing. ” Abstract page for arXiv paper 2602. 2018. The COVID-19 pandemic has amplified the demand for contactless Their paper, submitted to the ACM in February 2026 (arXiv:2602. Subsequently, we train various graph neural networks (GNNs), specifically graph convolutional networks (GCNs), GraphSAGE, and graph attention networks (GATs), to solve multi-class audio classification problems. 5 to the positive class, and the rest to the negative class. A threshold, set to 0. We propose a novel approach that leverages frequency-domain representations of sound signals to improve diagnostic accuracy. Although this method can achieve improvements in small-scale tasks, overall performance growth is slow and has not fully tapped the potential of quantum computing. An end-to-end example and architecture for Audio Deep Learning's foundational application scenario, in Plain English. It analyzes various ensemble deep learning techniques for audio classification in the medical field, highlighting how these methods address the challenges of environmental sound detection and classification, particularly in medical applications. The proposed method uses a hybrid combination of Mel-frequency cepstral coefficients (MFCCs), Wavelet scattering, ensemble learning, convolutional neural network (CNN) classification methods. “Understanding the difficulty of training deep feedforward neural networks. org/abs/1904. Aug 1, 2025 · We propose a novel hybrid CNN-LSTM architecture to address the challenges in Environmental Sound Classification (ESC), effectively capturing both spatial and temporal dependencies in audio signals. In this paper, we proposed a sound classification mechanism based on convolutional neural networks and used the MFCC sound feature extraction method to convert sound signals into spectrograms. Neural networks allow programs to recognize patterns and solve common problems in artificial intelligence, machine learning and deep learning. Yang, W. Alduraibi, L. The model was implemented with a Convolutional Neural Network (CNN) on a selected dataset for better classification. Experiment results show sound spectrum achieving comparable results in Convolutional Neural Network (CNN), with better predictions than its MFCC counterpart. 5, would assign samples of outputs larger or equal 0. (NASDAQ: WiMi) (“WiMi” or the “Company”), a leading global Hologram Augmented Reality (“AR”) Technology provider, proposed a brand-new technological innovation—a hybrid quantum-classical Inception neural network model for image classification. Kui, J. This is a brand-new hybrid architecture that naturally integrates quantum computing 17. For the identification of the environmental sounds, urban sound excerpts from the UrbanSound8K dataset were selected, as well as a convolutional neural network model and two audio data augmentation techniques. Seidaliyeva, M. We used two datasets for our experiment: ESC-10 and ESC-50. However, previous systems are built on specific datasets In order to learn time and frequency features from Log-Mel spectrogram more effectively, a temporal-frequency attention based convolutional neural network model (TFCNN) is proposed in this paper. In this work, an attempt has been made to classify emotional states using Electrodermal Activity (EDA) signals and Convolutional Neural Netw BEIJING, Feb. This CNN model can analyze various features of sound data, such as statistical features (e. Glorot, Xavier, and Yoshua Bengio. The convolutional neural networks (CNNs) lead in the domain of Sound Recognition due to its flexibility and ability with different adjusting parameters. . The experimental results indicate that the taxonomic accuracy of the proposed architectures can surpass the existing methods of CNNs with single feature extraction methods. For binary classification, f (x) passes through the logistic function g (z) = 1 / (1 + e z) to obtain output values between zero and one. This is a brand-new hybrid architecture that naturally integrates quantum The neural network models demonstrated a remarkable ability to classify distinct behavioral patterns associated with different approaches to maze navigation. “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. That means if If the CNN learns the dog in the left corner of the image above, then it can identify the dog in the ot Jan 8, 2019 · We used the spectrogram images of environmental sounds to train the convolutional neural network (CNN) and the tensor deep stacking network (TDSN). This is explained This study examines the application of deep neural networks (DNNs) and Bayesian neural networks (BNNs) for diagnosing motor faults through acoustic signal analysis. The project compares classical ML models, neural networks, and CNN-based audio models under a unified feature extraction framework. U. Zong, H. 08990 It can deal with audio signals of any length as it splits the signal into overlapped frames using a sliding window, hence no data augmentation is required. In this paper, we proposed a sound classification mechanism based on convolutional neural networks and used the MFCC sound feature extraction method to convert sound signals into spectrograms. The paper presents an analysis of classical deep neural network architectures for image labeling. In these studies, the obtained results show that the CNN classification gives the better result with 97. WiMi Hologram Cloud Inc. Urban Sound Classification End-to-end machine learning and deep learning pipeline for environmental sound recognition using the UrbanSound8K dataset. To get started, load the necessary inputs: Then the dataframe: Jan 8, 2019 · We used the spectrogram images of environmental sounds to train the convolutional neural network (CNN) and the tensor deep stacking network (TDSN). g. That’s because they can learn patterns that are translation invariant and have spatial hierarchies (F. , Anjali, T Both Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) were used for classification to compare obtained results. Abstract This paper proposes a pre-processing method for heart sound screening and extracts the high-order spectral feature of phonocardiogram. 9% classification accuracy according to the results of ANN. h6kl4, vlk8y, sccpfd, kgblv, iwpr, bxycv, dqgp, a2jj4, 0lax5, czjry,