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Found 31 papers, 12 papers with code
Inter-Speaker Relative Cues for Text-Guided Target Speech Extraction
We propose a novel approach that utilizes inter-speaker relative cues to distinguish target speakers and extract their voices from mixtures.
Attractor-Based Speech Separation of Multiple Utterances by Unknown Number of Speakers
We propose a speech separation model that simultaneously performs separation, dynamically estimates the number of speakers, and detects individual speaker activities by integrating an attractor module.
Score-informed Music Source Separation: Improving Synthetic-to-real Generalization in Classical Music
Music source separation is the task of separating a mixture of instruments into constituent tracks.
Gen-A: Generalizing Ambisonics Neural Encoding to Unseen Microphone Arrays
Using deep neural networks (DNNs) for encoding of microphone array (MA) signals to the Ambisonics spatial audio format can surpass certain limitations of established conventional methods, but existing DNN-based methods need to be trained separately for each MA.
Class-Incremental Learning for Sound Event Localization and Detection
This paper investigates the feasibility of class-incremental learning (CIL) for Sound Event Localization and Detection (SELD) tasks.
SynthSOD: Developing an Heterogeneous Dataset for Orchestra Music Source Separation
Recent advancements in music source separation have significantly progressed, particularly in isolating vocals, drums, and bass elements from mixed tracks.
Gaunt coefficients for complex and real spherical harmonics with applications to spherical array processing and Ambisonics
Additionally, the report provides a derivation of the Gaunt coefficients for real SHs, which has been missing from the literature and can be used directly in spatial audio frameworks such as Ambisonics.
Reference Channel Selection by Multi-Channel Masking for End-to-End Multi-Channel Speech Enhancement
The experimental results on the Spear challenge simulated dataset D4 demonstrate the superiority of our proposed method over the conventional approach of using a fixed reference channel with single-channel masking.
Speaker Distance Estimation in Enclosures from Single-Channel Audio
Distance estimation from audio plays a crucial role in various applications, such as acoustic scene analysis, sound source localization, and room modeling.
Sound Event Detection and Localization with Distance Estimation
Sound Event Detection and Localization (SELD) is a combined task of identifying sound events and their corresponding direction-of-arrival (DOA).
Perceptually-motivated Spatial Audio Codec for Higher-Order Ambisonics Compression
Scene-based spatial audio formats, such as Ambisonics, are playback system agnostic and may therefore be favoured for delivering immersive audio experiences to a wide range of (potentially unknown) devices.
Neural Ambisonics encoding for compact irregular microphone arrays
Ambisonics encoding of microphone array signals can enable various spatial audio applications, such as virtual reality or telepresence, but it is typically designed for uniformly-spaced spherical microphone arrays.
Attention-Driven Multichannel Speech Enhancement in Moving Sound Source Scenarios
The clean speech clips from WSJ0 are employed for simulating speech signals of moving speakers in a reverberant environment.
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.
Permutation Invariant Recurrent Neural Networks for Sound Source Tracking Applications
Conventional recurrent neural networks (RNNs), such as the long short-term memories (LSTMs) or the gated recurrent units (GRUs), take a vector as their input and use another vector to store their state.
Multi-Channel Masking with Learnable Filterbank for Sound Source Separation
Specifically, each mask is used to multiply the corresponding channel's 2D representation, and the masked output of all channels are then summed.
Position tracking of a varying number of sound sources with sliding permutation invariant training
Recent data- and learning-based sound source localization (SSL) methods have shown strong performance in challenging acoustic scenarios.
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
Self-supervised Learning of Audio Representations from Audio-Visual Data using Spatial Alignment
In addition to the correspondence, AVSA also learns from the spatial location of acoustic and visual content.
Differentiable Tracking-Based Training of Deep Learning Sound Source Localizers
Data-based and learning-based sound source localization (SSL) has shown promising results in challenging conditions, and is commonly set as a classification or a regression problem.
Joint Direction and Proximity Classification of Overlapping Sound Events from Binaural Audio
Finally, we propose various ways of combining the proximity and direction estimation problems into a joint task providing temporal information about the onsets and offsets of the appearing sources.
Mobile Microphone Array Speech Detection and Localization in Diverse Everyday Environments
Joint sound event localization and detection (SELD) is an integral part of developing context awareness into communication interfaces of mobile robots, smartphones, and home assistants.
Deep neural network Based Low-latency Speech Separation with Asymmetric analysis-Synthesis Window Pair
Time-frequency masking or spectrum prediction computed via short symmetric windows are commonly used in low-latency deep neural network (DNN) based source separation.
A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection
This report presents the dataset and baseline of Task 3 of the DCASE2021 Challenge on Sound Event Localization and Detection (SELD).
Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019
A large-scale realistic dataset of spatialized sound events was generated for the challenge, to be used for training of learning-based approaches, and for evaluation of the submissions in an unlabeled subset.
A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection
This report presents the dataset and the evaluation setup of the Sound Event Localization & Detection (SELD) task for the DCASE 2020 Challenge.
A multi-room reverberant dataset for sound event localization and detection
This paper presents the sound event localization and detection (SELD) task setup for the DCASE 2019 challenge.
Sound Audio and Speech Processing
Localization, Detection and Tracking of Multiple Moving Sound Sources with a Convolutional Recurrent Neural Network
This paper investigates the joint localization, detection, and tracking of sound events using a convolutional recurrent neural network (CRNN).
Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks
In this paper, we propose a convolutional recurrent neural network for joint sound event localization and detection (SELD) of multiple overlapping sound events in three-dimensional (3D) space.
Sound Audio and Speech Processing
Multichannel Sound Event Detection Using 3D Convolutional Neural Networks for Learning Inter-channel Features
Each of this dataset has a four-channel first-order Ambisonic, binaural, and single-channel versions, on which the performance of SED using the proposed method are compared to study the potential of SED using multichannel audio.
Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network
This paper proposes a deep neural network for estimating the directions of arrival (DOA) of multiple sound sources.
