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Found 21 papers, 14 papers with code
Exploring Pre-trained General-purpose Audio Representations for Heart Murmur Detection
To reduce the need for skilled clinicians in heart sound interpretation, recent studies on automating cardiac auscultation have explored deep learning approaches.
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework
This study proposes Masked Modeling Duo (M2D), an improved masked prediction SSL, which learns by predicting representations of masked input signals that serve as training signals.
Refining Knowledge Transfer on Audio-Image Temporal Agreement for Audio-Text Cross Retrieval
Experimental results show that method (i) improves the audio-text retrieval performance by selecting the nearest image that aligns with the audio information and transferring the learned knowledge.
Unrestricted Global Phase Bias-Aware Single-channel Speech Enhancement with Conformer-based Metric GAN
With the rapid development of neural networks in recent years, the ability of various networks to enhance the magnitude spectrum of noisy speech in the single-channel speech enhancement domain has become exceptionally outstanding.
Audio Difference Captioning Utilizing Similarity-Discrepancy Disentanglement
We proposed Audio Difference Captioning (ADC) as a new extension task of audio captioning for describing the semantic differences between input pairs of similar but slightly different audio clips.
Masked Modeling Duo for Speech: Specializing General-Purpose Audio Representation to Speech using Denoising Distillation
Self-supervised learning general-purpose audio representations have demonstrated high performance in a variety of tasks.
Deep sound-field denoiser: optically-measured sound-field denoising using deep neural network
We compared the method with conventional ones, such as image filters, a spatiotemporal filter, and other DNN architectures, on numerical and experimental data.
First-shot anomaly sound detection for machine condition monitoring: A domain generalization baseline
This paper provides a baseline system for First-shot-compliant unsupervised anomaly detection (ASD) for machine condition monitoring.
Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input
We propose a new method, Masked Modeling Duo (M2D), that learns representations directly while obtaining training signals using only masked patches.
Ranked #1 on
Speaker Identification
on VoxCeleb1
(using extra training data)
ConceptBeam: Concept Driven Target Speech Extraction
We use it to bridge modality-dependent information, i. e., the speech segments in the mixture, and the specified, modality-independent concept.
Introducing Auxiliary Text Query-modifier to Content-based Audio Retrieval
While the range of conventional content-based audio retrieval is limited to audio that is similar to the query audio, the proposed method can adjust the retrieval range by adding an embedding of the auxiliary text query-modifier to the embedding of the query sample audio in a shared latent space.
Composing General Audio Representation by Fusing Multilayer Features of a Pre-trained Model
This approach improves the utility of frequency and channel information in downstream processes, and combines the effectiveness of middle and late layer features for different tasks.
Masked Spectrogram Modeling using Masked Autoencoders for Learning General-purpose Audio Representation
In this paper, we seek to learn audio representations from the input itself as supervision using a pretext task of auto-encoding of masked spectrogram patches, Masked Spectrogram Modeling (MSM, a variant of Masked Image Modeling applied to audio spectrogram).
BYOL for Audio: Exploring Pre-trained General-purpose Audio Representations
In this study, we hypothesize that representations effective for general audio tasks should provide multiple aspects of robust features of the input sound.
ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions
This paper proposes a new large-scale dataset called "ToyADMOS2" for anomaly detection in machine operating sounds (ADMOS).
BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation
Inspired by the recent progress in self-supervised learning for computer vision that generates supervision using data augmentations, we explore a new general-purpose audio representation learning approach.
Audio Captioning using Pre-Trained Large-Scale Language Model Guided by Audio-based Similar Caption Retrieval
Then, the caption of the audio input is generated by using a pre-trained language model while referring to the guidance captions.
Effects of Word-frequency based Pre- and Post- Processings for Audio Captioning
The system we used for Task 6 (Automated Audio Captioning)of the Detection and Classification of Acoustic Scenes and Events(DCASE) 2020 Challenge combines three elements, namely, dataaugmentation, multi-task learning, and post-processing, for audiocaptioning.
The NTT DCASE2020 Challenge Task 6 system: Automated Audio Captioning with Keywords and Sentence Length Estimation
This technical report describes the system participating to the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge, Task 6: automated audio captioning.
Ranked #4 on
Audio captioning
on Clotho
Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention
This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features; we extract a speaker representation used for adaptation directly from the test utterance.
Invertible DNN-based nonlinear time-frequency transform for speech enhancement
Therefore, some end-to-end methods used a DNN to learn the linear T-F transform which is much easier to understand.
Audio and Speech Processing Sound
