Papadimitriou et al., 2020 - Google Patents
Audio-based event detection at different SNR settings using two-dimensional spectrogram magnitude representationsPapadimitriou et al., 2020
View HTML- Document ID
- 6731624358800836474
- Author
- Papadimitriou I
- Vafeiadis A
- Lalas A
- Votis K
- Tzovaras D
- Publication year
- Publication venue
- Electronics
External Links
Snippet
Audio-based event detection poses a number of different challenges that are not encountered in other fields, such as image detection. Challenges such as ambient noise, low Signal-to-Noise Ratio (SNR) and microphone distance are not yet fully understood. If the …
- 238000001514 detection method 0 title abstract description 25
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
- G06K9/6217—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
- 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
- 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
- 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
- G06Q10/00—Administration; Management
-
- 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
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Su et al. | Environment sound classification using a two-stream CNN based on decision-level fusion | |
Papadimitriou et al. | Audio-based event detection at different SNR settings using two-dimensional spectrogram magnitude representations | |
Nam et al. | Cascaded convolutional neural network architecture for speech emotion recognition in noisy conditions | |
Luque et al. | Evaluation of MPEG-7-based audio descriptors for animal voice recognition over wireless acoustic sensor networks | |
Zhang et al. | Bird species identification using spectrogram based on multi-channel fusion of DCNNs | |
Lee et al. | CNN-based acoustic scene classification system | |
Qin et al. | Source cell-phone identification in the presence of additive noise from CQT domain | |
Grollmisch et al. | Improving semi-supervised learning for audio classification with FixMatch | |
Nogueira et al. | Sound classification and processing of urban environments: A systematic literature review | |
Choi et al. | Noise-robust sound-event classification system with texture analysis | |
Yang et al. | ResNet based on multi-feature attention mechanism for sound classification in noisy environments | |
Shin et al. | Self-supervised transfer learning from natural images for sound classification | |
Naranjo-Alcazar et al. | Open set audio classification using autoencoders trained on few data | |
Ghani et al. | A randomized bag-of-birds approach to study robustness of automated audio based bird species classification | |
Guerrieri et al. | Gender identification in a two-level hierarchical speech emotion recognition system for an Italian Social Robot | |
Hajihashemi et al. | Binaural acoustic scene classification using wavelet scattering, parallel ensemble classifiers and nonlinear fusion | |
Wang et al. | Unsupervised anomalous sound detection for machine condition monitoring using classification-based methods | |
Lee et al. | A preprocessing strategy for denoising of speech data based on speech segment detection | |
Zhang et al. | A novel bird sound recognition method based on multifeature fusion and a transformer encoder | |
He et al. | Multimodal Sensor-Input Architecture with Deep Learning for Audio-Visual Speech Recognition in Wild | |
Lambamo et al. | Analyzing noise robustness of cochleogram and Mel spectrogram features in deep learning based speaker recognition | |
Tey et al. | Cicada species recognition based on acoustic signals | |
Cai et al. | Blind image quality assessment based on classification guidance and feature aggregation | |
Diez et al. | NoisenseDB: An Urban Sound Event Database to Develop Neural Classification Systems for Noise-Monitoring Applications | |
Kasnesis et al. | Acoustic sensor data flow for cultural heritage monitoring and safeguarding |