A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper detects adversarial attacks in sound classification models.
Review of deep learning techniques for audio signal processing.
New dataset for industrial machine malfunction detection with domain shifts.
DCASE 2021 ASD task tackles domain-shifted anomalous sound detection.
Environmental acoustic sensing involves the retrieval and processing of audio signals to better understand our surroundings. While large-scale acoustic data make manual analysis infeasible, they provide a suitable playground for machine learning approaches. Most existing machine learning techniques developed for enviro…
End-to-end CNN classifies environmental sounds from audio signals.
Bird sounds possess distinctive spectral structure which may exhibit small shifts in spectrum depending on the bird species and environmental conditions. In this paper, we propose using convolutional recurrent neural networks on the task of automated bird audio detection in real-life environments. In the proposed metho…
In this paper we propose a novel environmental sound classification approach incorporating unsupervised feature learning from codebook via spherical -Means++ algorithm and a new architecture for high-level data augmentation. The audio signal is transformed into a 2D representation using a discrete wavelet transform …
Neural network based architectures used for sound recognition are usually adapted from other application domains, which may not harness sound related properties. The ConditionaL Neural Network (CLNN) is designed to consider the relational properties across frames in a temporal signal, and its extension the Masked Condi…
Model for directed synthesis of audio textures using multi-scale RNNs.
The ConditionaL Neural Network (CLNN) exploits the nature of the temporal sequencing of the sound signal represented in a spectrogram, and its variant the Masked ConditionaL Neural Network (MCLNN) induces the network to learn in frequency bands by embedding a filterbank-like sparseness over the network's links using a …
This paper introduces a model of environmental acoustic scenes which adopts a morphological approach by ab-stracting temporal structures of acoustic scenes. To demonstrate its potential, this model is employed to evaluate the performance of a large set of acoustic events detection systems. This model allows us to expli…
A new method detects anomalous sounds using self-supervised learning.
Paper introduces ToyADMOS dataset for detecting anomalous machine sounds.
Paper proposes active learning for sound event detection with reduced annotation effort.
This paper proposes a novel optimization principle and its implementation for unsupervised anomaly detection in sound (ADS) using an autoencoder (AE). The goal of unsupervised-ADS is to detect unknown anomalous sound without training data of anomalous sound. Use of an AE as a normal model is a state-of-the-art techniqu…
New method separates sounds with weak labels in noisy environments.
The paper presents a method for sound event localization and detection using CRNN models.
In this study we present a kernel based convolution model to characterize neural responses to natural sounds by decoding their time-varying acoustic features. The model allows to decode natural sounds from high-dimensional neural recordings, such as magnetoencephalography (MEG), that track timing and location of human …
Paper improves sound event detection using semi-supervised learning.
Batch uniformization improves anomaly detection in sound data.
The paper detects amateur drones using acoustic signals, overcoming interference.
Paper discusses ASD challenge for machine condition monitoring.
Deep neural network architectures designed for application domains other than sound, especially image recognition, may not optimally harness the time-frequency representation when adapted to the sound recognition problem. In this work, we explore the ConditionaL Neural Network (CLNN) and the Masked ConditionaL Neural N…
Reduces sound event localization error by 2.6x with hybrid parametric-deep learning.
Proposes a DOA estimation method using IVs and DNNs for noise and reverberation reduction.
A method for semi-supervised sound event detection using teacher-student learning.
As part of the 2016 public evaluation challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2016), the second task focused on evaluating sound event detection systems using synthetic mixtures of office sounds. This task, which follows the `Event Detection - Office Synthetic' task of DCASE 2013,…
Automatically detecting sound units of humpback whales in complex time-varying background noises is a current challenge for scientists. In this paper, we explore the applicability of Convolution Neural Network (CNN) method for this task. In the evaluation stage, we present 6 bi-class classification experimentations of …
SELD-TCN improves sound event localization and detection efficiency.
Improved audio event recognition using audiovisual transformers.
Paper presents a deep learning framework for classifying respiratory anomalies and lung diseases from sound recordings.
Automatic Music Transcription (AMT) is one of the oldest and most well-studied problems in the field of music information retrieval. Within this challenging research field, onset detection and instrument recognition take important places in transcription systems, as they respectively help to determine exact onset times…
Sound event detection systems typically consist of two stages: extracting hand-crafted features from the raw audio waveform, and learning a mapping between these features and the target sound events using a classifier. Recently, the focus of sound event detection research has been mostly shifted to the latter stage usi…
Smart city surveillance benefits from sound event recognition.
Deep learning model detects and corrects outliers in crowd-sourced weather data.
Sound event detection (SED) methods are tasked with labeling segments of audio recordings by the presence of active sound sources. SED is typically posed as a supervised machine learning problem, requiring strong annotations for the presence or absence of each sound source at every time instant within the recording. Ho…
New dataset for industrial machine sounds to aid maintenance.
New method uses topological data analysis for better change point detection.
Heart diseases constitute a global health burden, and the problem is exacerbated by the error-prone nature of listening to and interpreting heart sounds. This motivates the development of automated classification to screen for abnormal heart sounds. Existing machine learning-based systems achieve accurate classificatio…
Biodiversity monitoring using audio recordings is achievable at a truly global scale via large-scale deployment of inexpensive, unattended recording stations or by large-scale crowdsourcing using recording and species recognition on mobile devices. The ability, however, to reliably identify vocalising animal species is…
Automatic feature extraction using neural networks has accomplished remarkable success for images, but for sound recognition, these models are usually modified to fit the nature of the multi-dimensional temporal representation of the audio signal in spectrograms. This may not efficiently harness the time-frequency repr…
New method uses tensor decomposition to improve noise reduction in machine fault detection.
Paper proposes a technique to detect and predict sources of contaminants in complex systems.
Adversarial training improves audio classifier robustness.
End-to-end neural network based approaches to audio modelling are generally outperformed by models trained on high-level data representations. In this paper we present preliminary work that shows the feasibility of training the first layers of a deep convolutional neural network (CNN) model to learn the commonly-used l…
Novel method segments heart sound signals using LSTMs with attention.
Paper proposes MTL for weakly labelled SED, improving performance with 2-step attention.