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Found 26 papers, 12 papers with code
NIPS4Bplus: a richly annotated birdsong audio dataset
Recent advances in birdsong detection and classification have approached a limit due to the lack of fully annotated recordings.
Sound Digital Libraries Audio and Speech Processing
Few-shot bioacoustic event detection at the DCASE 2023 challenge
Few-shot bioacoustic event detection consists in detecting sound events of specified types, in varying soundscapes, while having access to only a few examples of the class of interest.
Mind the Domain Gap: a Systematic Analysis on Bioacoustic Sound Event Detection
A recent development in the field is the introduction of the task known as few-shot bioacoustic sound event detection, which aims to train a versatile animal sound detector using only a small set of audio samples.
Unifying Probabilistic Models for Time-Frequency Analysis
In audio signal processing, probabilistic time-frequency models have many benefits over their non-probabilistic counterparts.
Efficient On-line Computation of Visibility Graphs
The empirical evidence suggests the proposed method for computation of visibility graphs offers an on-line computation solution at no additional computation time cost.
Data Structures and Algorithms
Sparse Gaussian Process Audio Source Separation Using Spectrum Priors in the Time-Domain
As a result, source separation GP models have been restricted to the analysis of short audio frames.
Visibility graphs for robust harmonic similarity measures between audio spectra
We present experiments demonstrating the utility of this distance measure for real and synthesised audio data.
Sound Audio and Speech Processing
Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks
Despite the popularity of guitar effects, there is very little existing research on classification and parameter estimation of specific plugins or effect units from guitar recordings.
End-to-End Probabilistic Inference for Nonstationary Audio Analysis
A typical audio signal processing pipeline includes multiple disjoint analysis stages, including calculation of a time-frequency representation followed by spectrogram-based feature analysis.
Auto deep learning for bioacoustic signals
This study investigates the potential of automated deep learning to enhance the accuracy and efficiency of multi-class classification of bird vocalizations, compared against traditional manually-designed deep learning models.
Efficient Learning of Harmonic Priors for Pitch Detection in Polyphonic Music
Automatic music transcription (AMT) aims to infer a latent symbolic representation of a piece of music (piano-roll), given a corresponding observed audio recording.
Rank-based loss for learning hierarchical representations
We show that rank based loss is suitable to learn hierarchical representations of the data.
A Generative Model for Natural Sounds Based on Latent Force Modelling
Recent advances in analysis of subband amplitude envelopes of natural sounds have resulted in convincing synthesis, showing subband amplitudes to be a crucial component of perception.
Gaussian Processes for Music Audio Modelling and Content Analysis
We present a Bayesian approach for modelling music audio, and content analysis.
Denoising without access to clean data using a partitioned autoencoder
Training a denoising autoencoder neural network requires access to truly clean data, a requirement which is often impractical.
Acoustic Scene Classification
We then describe a range of different algorithms submitted for a data challenge that was held to provide a general and fair benchmark for ASC techniques.
Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning
Feature learning can be performed at large scale and "unsupervised", meaning it requires no manual data labelling, yet it can improve performance on "supervised" tasks such as classification.
Deep Learning for Audio Transcription on Low-Resource Datasets
In training a deep learning system to perform audio transcription, two practical problems may arise.
Data-Efficient Weakly Supervised Learning for Low-Resource Audio Event Detection Using Deep Learning
We propose a method to perform audio event detection under the common constraint that only limited training data are available.
Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge
Assessing the presence and abundance of birds is important for monitoring specific species as well as overall ecosystem health.
Sound Audio and Speech Processing
Bird detection in audio: a survey and a challenge
Many biological monitoring projects rely on acoustic detection of birds.
Sound
Short-term prediction of photovoltaic power generation using Gaussian process regression
The present paper focuses on evaluating predictions of the energy generated by PV systems in the United Kingdom Gaussian process regression (GPR).
Comparison between transformers and convolutional models for fine-grained classification of insects
Although we observe high performances with all three families of models, our analysis shows that the hybrid model outperforms the fully convolutional-base and fully transformer-base models on accuracy performance and the fully transformer-base model outperforms the others on inference speed and, these prove the transformer to be robust to the shortage of samples and to be faster at inference time.
ATGNN: Audio Tagging Graph Neural Network
Deep learning models such as CNNs and Transformers have achieved impressive performance for end-to-end audio tagging.
Efficient speech detection in environmental audio using acoustic recognition and knowledge distillation
Our findings revealed that the distilled models exhibited comparable performance to the EcoVAD teacher model, indicating a promising approach to overcoming computational barriers for real-time ecological monitoring.
Performance of computer vision algorithms for fine-grained classification using crowdsourced insect images
The field of computer vision offers a wide range of algorithms, each with its strengths and weaknesses; how do we identify the algorithm that is in line with our application?
