859 citations · 3.5k across the 72 of their papers we have counts for
14 papers · 1 filter
VOICe: A Sound Event Detection Dataset For Generalizable Domain Adaptation
Shayan Gharib, Konstantinos Drossos, Eemi Fagerlund +1
The performance of sound event detection methods can significantly degrade when they are used in unseen conditions (e.g. recording devices, ambient noise). Domain adaptation is a p…
Sound event detection via dilated convolutional recurrent neural networks
Yanxiong Li, Mingle Liu, Konstantinos Drossos +1
Convolutional recurrent neural networks (CRNNs) have achieved state-of-the-art performance for sound event detection (SED). In this paper, we propose to use a dilated CRNN, namely…
Online Spectrogram Inversion for Low-Latency Audio Source Separation
Paul Magron, Tuomas Virtanen
Audio source separation is usually achieved by estimating the short-time Fourier transform (STFT) magnitude of each source, and then applying a spectrogram inversion algorithm to r…
Memory Requirement Reduction of Deep Neural Networks Using Low-bit Quantization of Parameters
Niccoló Nicodemo, Gaurav Naithani, Konstantinos Drossos +2
Effective employment of deep neural networks (DNNs) in mobile devices and embedded systems is hampered by requirements for memory and computational power. This paper presents a non…
Clotho: An Audio Captioning Dataset
Konstantinos Drossos, Samuel Lipping, Tuomas Virtanen
Audio captioning is the novel task of general audio content description using free text. It is an intermodal translation task (not speech-to-text), where a system accepts as an inp…
Crowdsourcing a Dataset of Audio Captions
Samuel Lipping, Konstantinos Drossos, Tuomas Virtanen
Audio captioning is a novel field of multi-modal translation and it is the task of creating a textual description of the content of an audio signal (e.g. "people talking in a big r…
Language Modelling for Sound Event Detection with Teacher Forcing and Scheduled Sampling
Konstantinos Drossos, Shayan Gharib, Paul Magron +1
A sound event detection (SED) method typically takes as an input a sequence of audio frames and predicts the activities of sound events in each frame. In real-life recordings, the…
Deep Learning for Audio Signal Processing
Hendrik Purwins, Bo Li, Tuomas Virtanen +3
Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music,…
A multi-room reverberant dataset for sound event localization and detection
Sharath Adavanne, Archontis Politis, Tuomas Virtanen
This paper presents the sound event localization and detection (SELD) task setup for the DCASE 2019 challenge. The goal of the SELD task is to detect the temporal activities of a k…
Zero-Shot Audio Classification Based on Class Label Embeddings
Huang Xie, Tuomas Virtanen
This paper proposes a zero-shot learning approach for audio classification based on the textual information about class labels without any audio samples from target classes. We pro…
City classification from multiple real-world sound scenes
Helen L. Bear, Toni Heittola, Annamaria Mesaros +2
The majority of sound scene analysis work focuses on one of two clearly defined tasks: acoustic scene classification or sound event detection. Whilst this separation of tasks is us…
Localization, Detection and Tracking of Multiple Moving Sound Sources with a Convolutional Recurrent Neural Network
Sharath Adavanne, Archontis Politis, Tuomas Virtanen
This paper investigates the joint localization, detection, and tracking of sound events using a convolutional recurrent neural network (CRNN). We use a CRNN previously proposed for…
Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification
Konstantinos Drossos, Paul Magron, Tuomas Virtanen
A challenging problem in deep learning-based machine listening field is the degradation of the performance when using data from unseen conditions. In this paper we focus on the aco…
Low-Latency Deep Clustering For Speech Separation
Shanshan Wang, Gaurav Naithani, Tuomas Virtanen
This paper proposes a low algorithmic latency adaptation of the deep clustering approach to speaker-independent speech separation. It consists of three parts: a) the usage of long-…