Search Results for author: Hagen Wierstorf

Found 8 papers, 3 papers with code

Nkululeko: A Tool For Rapid Speaker Characteristics Detection

1 code implementation LREC 2022 Felix Burkhardt, Johannes Wagner, Hagen Wierstorf, Florian Eyben, Björn Schuller

We present advancements with a software tool called Nkululeko, that lets users perform (semi-) supervised machine learning experiments in the speaker characteristics domain.

Emotion Classification regression

Testing Speech Emotion Recognition Machine Learning Models

no code implementations11 Dec 2023 Anna Derington, Hagen Wierstorf, Ali Özkil, Florian Eyben, Felix Burkhardt, Björn W. Schuller

Machine learning models for speech emotion recognition (SER) can be trained for different tasks and are usually evaluated on the basis of a few available datasets per task.

Fairness Speech Emotion Recognition

audb -- Sharing and Versioning of Audio and Annotation Data in Python

1 code implementation1 Mar 2023 Hagen Wierstorf, Johannes Wagner, Florian Eyben, Felix Burkhardt, Björn W. Schuller

Driven by the need for larger and more diverse datasets to pre-train and fine-tune increasingly complex machine learning models, the number of datasets is rapidly growing.


Probing Speech Emotion Recognition Transformers for Linguistic Knowledge

no code implementations1 Apr 2022 Andreas Triantafyllopoulos, Johannes Wagner, Hagen Wierstorf, Maximilian Schmitt, Uwe Reichel, Florian Eyben, Felix Burkhardt, Björn W. Schuller

Large, pre-trained neural networks consisting of self-attention layers (transformers) have recently achieved state-of-the-art results on several speech emotion recognition (SER) datasets.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Referenceless Performance Evaluation of Audio Source Separation using Deep Neural Networks

no code implementations1 Nov 2018 Emad M. Grais, Hagen Wierstorf, Dominic Ward, Russell Mason, Mark D. Plumbley

Current performance evaluation for audio source separation depends on comparing the processed or separated signals with reference signals.

Audio Source Separation blind source separation

Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation

no code implementations28 Oct 2017 Emad M. Grais, Hagen Wierstorf, Dominic Ward, Mark D. Plumbley

In deep neural networks with convolutional layers, each layer typically has fixed-size/single-resolution receptive field (RF).

Audio Source Separation

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