-
Johannes Kepler University
- Linz
- in/florian-schmid-887224293
- @Florian04130962
- https://scholar.google.com/citations?user=BYQ5Sy8AAAAJ&hl=de
Stars
Prediction of sound event bounding boxes (SEBBs)
This project is for "Practical Work in AI"
This repository aims at providing efficient CNNs for Audio Tagging. We provide AudioSet pre-trained models ready for downstream training and extraction of audio embeddings.
A library built for easier audio self-supervised training, downstream tasks evaluation
This repository provides the code for "Improving Query-by-Vocal Imitation with Contrastive Learning and Audio Pretraining", presented at DCASE 2024. The paper addresses the challenge of audio retri…
This repository provides an easy way to train your models on the datasets of DCASE task 1.
This repo includes the official implementations of "Fine-tune the pretrained ATST model for sound event detection".
Web application to record speech for an open data set
Internet Explorer explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset.
State-of-the-art audio codec with 90x compression factor. Supports 44.1kHz, 24kHz, and 16kHz mono/stereo audio.
Vector (and Scalar) Quantization, in Pytorch
Code and slides of my YouTube series called "Audio Signal Proessing for Machine Learning"
Simple implementation of Mobile-Former on Pytorch
This repository contains the code of the CP JKU submission to DCASE23 Task 1 "Low-complexity Acoustic Scene Classification"
AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head
Summary, Code for Deep Neural Network Quantization
A lightweight library for Frechet Audio Distance calculation.
Collection of recent methods on (deep) neural network compression and acceleration.
Improving Recording Device Generalization using Impulse Response Augmentation
Source code for ICASSP2022 "Pseudo Strong labels for large scale weakly supervised audio tagging"
ESC-50: Dataset for Environmental Sound Classification
Evaluate EfficientAT models on the Holistic Evaluation of Audio Representations Benchmark.