Nogueira et al., 2022 - Google Patents
Sound classification and processing of urban environments: A systematic literature reviewNogueira et al., 2022
View HTML- Document ID
- 13377568670847325187
- Author
- Nogueira A
- Oliveira H
- Machado J
- Tavares J
- Publication year
- Publication venue
- Sensors
External Links
Snippet
Audio recognition can be used in smart cities for security, surveillance, manufacturing, autonomous vehicles, and noise mitigation, just to name a few. However, urban sounds are everyday audio events that occur daily, presenting unstructured characteristics containing …
- 230000003935 attention 0 abstract description 70
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination
- G10L25/66—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Abeßer | A review of deep learning based methods for acoustic scene classification | |
Petmezas et al. | Automated lung sound classification using a hybrid CNN-LSTM network and focal loss function | |
Hajarolasvadi et al. | 3D CNN-based speech emotion recognition using k-means clustering and spectrograms | |
Vázquez-Romero et al. | Automatic detection of depression in speech using ensemble convolutional neural networks | |
Kwasny et al. | Gender and age estimation methods based on speech using deep neural networks | |
Aziz et al. | Automatic scene recognition through acoustic classification for behavioral robotics | |
Nogueira et al. | Sound classification and processing of urban environments: A systematic literature review | |
Alsabhan | Human–computer interaction with a real-time speech emotion recognition with ensembling techniques 1D convolution neural network and attention | |
Abdusalomov et al. | Improved feature parameter extraction from speech signals using machine learning algorithm | |
Zhang et al. | Research on singing voice detection based on a long-term recurrent convolutional network with vocal separation and temporal smoothing | |
Lei et al. | Low-power audio keyword spotting using tsetlin machines | |
Nam et al. | Cascaded convolutional neural network architecture for speech emotion recognition in noisy conditions | |
Papadimitriou et al. | Audio-based event detection at different SNR settings using two-dimensional spectrogram magnitude representations | |
Zhou et al. | Adaptive noise reduction for sound event detection using subband-weighted NMF | |
Qin et al. | Source cell-phone identification in the presence of additive noise from CQT domain | |
Zhang et al. | Bird species identification using spectrogram based on multi-channel fusion of DCNNs | |
Grollmisch et al. | Improving semi-supervised learning for audio classification with FixMatch | |
Sekkate et al. | An investigation of a feature-level fusion for noisy speech emotion recognition | |
Shen et al. | Compression of a deep competitive network based on mutual information for underwater acoustic targets recognition | |
Bang et al. | Adaptive data boosting technique for robust personalized speech emotion in emotionally-imbalanced small-sample environments | |
Wu et al. | Speech enhancement using generative adversarial network by distilling knowledge from statistical method | |
Hajihashemi et al. | Binaural acoustic scene classification using wavelet scattering, parallel ensemble classifiers and nonlinear fusion | |
Guerrieri et al. | Gender identification in a two-level hierarchical speech emotion recognition system for an Italian Social Robot | |
Ba Wazir et al. | Design and implementation of fast spoken foul language recognition with different end-to-end deep neural network architectures | |
Liu et al. | Locally activated gated neural network for automatic music genre classification |