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Found 18 papers, 10 papers with code
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Cinematic Demixing Track
A significant source of this improvement was making the simulated data better match real cinematic audio, which we further investigate in detail.
STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes with Spatiotemporal Annotations of Sound Events
While direction of arrival (DOA) of sound events is generally estimated from multichannel audio data recorded in a microphone array, sound events usually derive from visually perceptible source objects, e. g., sounds of footsteps come from the feet of a walker.
Diffusion-Based Speech Enhancement with Joint Generative and Predictive Decoders
At the decoded feature level, we fuse the two decoded features by generative and predictive decoders.
The Whole Is Greater than the Sum of Its Parts: Improving DNN-based Music Source Separation
We modify the target network, i. e., the network architecture of the original DNN-based MSS, by adding bridging paths for each output instrument to share their information.
Diffusion-based Signal Refiner for Speech Separation
We experimentally show that our refiner can provide a clearer harmonic structure of speech and improves the reference-free metric of perceptual quality for arbitrary preceding model architectures.
Diffiner: A Versatile Diffusion-based Generative Refiner for Speech Enhancement
Although deep neural network (DNN)-based speech enhancement (SE) methods outperform the previous non-DNN-based ones, they often degrade the perceptual quality of generated outputs.
DiffRoll: Diffusion-based Generative Music Transcription with Unsupervised Pretraining Capability
In this paper we propose a novel generative approach, DiffRoll, to tackle automatic music transcription (AMT).
STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events
Additionally, the report presents the baseline system that accompanies the dataset in the challenge with emphasis on the differences with the baseline of the previous iterations; namely, introduction of the multi-ACCDOA representation to handle multiple simultaneous occurences of events of the same class, and support for additional improved input features for the microphone array format.
Ranked #1 on
Sound Event Localization and Detection
on STARSS22
SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization
In this paper, we propose a new training scheme that extends the standard VAE via novel stochastic dequantization and quantization, called stochastically quantized variational autoencoder (SQ-VAE).
Multi-ACCDOA: Localizing and Detecting Overlapping Sounds from the Same Class with Auxiliary Duplicating Permutation Invariant Training
The multi- ACCDOA format (a class- and track-wise output format) enables the model to solve the cases with overlaps from the same class.
Improving Character Error Rate Is Not Equal to Having Clean Speech: Speech Enhancement for ASR Systems with Black-box Acoustic Models
In order to optimize the DNN-based SE model in terms of the character error rate (CER), which is one of the metric to evaluate the ASR system and generally non-differentiable, our method uses two DNNs: one for speech processing and one for mimicking the output CERs derived through an acoustic model (AM).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Spatial mixup: Directional loudness modification as data augmentation for sound event localization and detection
Data augmentation methods have shown great importance in diverse supervised learning problems where labeled data is scarce or costly to obtain.
Ensemble of ACCDOA- and EINV2-based Systems with D3Nets and Impulse Response Simulation for Sound Event Localization and Detection
This report describes our systems submitted to the DCASE2021 challenge task 3: sound event localization and detection (SELD) with directional interference.
Manifold-Aware Deep Clustering: Maximizing Angles between Embedding Vectors Based on Regular Simplex
This paper presents a new deep clustering (DC) method called manifold-aware DC (M-DC) that can enhance hyperspace utilization more effectively than the original DC.
Preventing Oversmoothing in VAE via Generalized Variance Parameterization
Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon in which the learned latent space becomes uninformative.
AR-ELBO: Preventing Posterior Collapse Induced by Oversmoothing in Gaussian VAE
Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon that the learned latent space becomes uninformative.
ACCDOA: Activity-Coupled Cartesian Direction of Arrival Representation for Sound Event Localization and Detection
Conventional NN-based methods use two branches for a sound event detection (SED) target and a direction-of-arrival (DOA) target.
All for One and One for All: Improving Music Separation by Bridging Networks
This paper proposes several improvements for music separation with deep neural networks (DNNs), namely a multi-domain loss (MDL) and two combination schemes.
Ranked #21 on
Music Source Separation
on MUSDB18
