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Found 10 papers, 7 papers with code
Auditory Neural Response Inspired Sound Event Detection Based on Spectro-temporal Receptive Field
In this work, we utilized STRF as a kernel of the first convolutional layer in SED model to extract neural response from input sound to make SED model similar to human auditory system.
VIFS: An End-to-End Variational Inference for Foley Sound Synthesis
Different from TTS models which generate short pronunciation from phonemes and speaker identity, the category-to-sound problem requires generating diverse sounds just from a category index.
Data Augmentation and Squeeze-and-Excitation Network on Multiple Dimension for Sound Event Localization and Detection in Real Scenes
Performance of sound event localization and detection (SELD) in real scenes is limited by small size of SELD dataset, due to difficulty in obtaining sufficient amount of realistic multi-channel audio data recordings with accurate label.
Frequency Dependent Sound Event Detection for DCASE 2022 Challenge Task 4
While many deep learning methods on other domains have been applied to sound event detection (SED), differences between original domains of the methods and SED have not been appropriately considered so far.
Frequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition for Sound Event Detection
2D convolution is widely used in sound event detection (SED) to recognize two dimensional time-frequency patterns of sound events.
Ranked #3 on
Sound Event Detection
on DESED
Decomposed Temporal Dynamic CNN: Efficient Time-Adaptive Network for Text-Independent Speaker Verification Explained with Speaker Activation Map
To extract accurate speaker information for text-independent speaker verification, temporal dynamic CNNs (TDY-CNNs) adapting kernels to each time bin was proposed.
FilterAugment: An Acoustic Environmental Data Augmentation Method
Thus, training acoustic models for audio and speech tasks requires regularization on various acoustic environments in order to achieve robust performance in real life applications.
Temporal Dynamic Convolutional Neural Network for Text-Independent Speaker Verification and Phonemetic Analysis
The temporal dynamic model adapts itself to phonemes without explicitly given phoneme information during training, and results show the necessity to consider phoneme variation within utterances for more accurate and robust text-independent speaker verification.
Deep learning based cough detection camera using enhanced features
Finally, a test F1 score of 91. 9% (test accuracy of 97. 2%) was achieved from G-net with the MFCC-V-A feature (named Spectroflow), an acoustic feature effective for use in cough detection.
Heavily Augmented Sound Event Detection utilizing Weak Predictions
In this work, we used two main approaches to overcome the lack of strongly labeled data.
Ranked #6 on
Sound Event Detection
on DESED
