Search Results for author:
Found 32 papers, 17 papers with code
Music auto-tagging in the long tail: A few-shot approach
We propose to integrate few-shot learning methodology into multi-label music auto-tagging by using features from pre-trained models as inputs to a lightweight linear classifier, also known as a linear probe.
A Stem-Agnostic Single-Decoder System for Music Source Separation Beyond Four Stems
Of the very few current systems that support source separation beyond this setup, most continue to rely on an inflexible decoder setup that can only support a fixed pre-defined set of stems.
Understanding Pedestrian Movement Using Urban Sensing Technologies: The Promise of Audio-based Sensors
This study discusses a new approach to scale up urban sensing of people with the help of novel audio-based technology.
Embedding Compression for Teacher-to-Student Knowledge Transfer
Common knowledge distillation methods require the teacher model and the student model to be trained on the same task.
ASPED: An Audio Dataset for Detecting Pedestrians
We introduce the new audio analysis task of pedestrian detection and present a new large-scale dataset for this task.
A Generalized Bandsplit Neural Network for Cinematic Audio Source Separation
Cinematic audio source separation is a relatively new subtask of audio source separation, with the aim of extracting the dialogue, music, and effects stems from their mixture.
Audio Embeddings as Teachers for Music Classification
Music classification has been one of the most popular tasks in the field of music information retrieval.
Ranked #2 on
Instrument Recognition
on OpenMIC-2018
Quantifying Spatial Audio Quality Impairment
Spatial audio quality is a highly multifaceted concept, with many interactions between environmental, geometrical, anatomical, psychological, and contextual considerations.
Music Instrument Classification Reprogrammed
The performance of approaches to Music Instrument Classification, a popular task in Music Information Retrieval, is often impacted and limited by the lack of availability of annotated data for training.
Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming
In this work, we introduce a novel method for leveraging pre-trained models for low-resource (music) classification based on the concept of Neural Model Reprogramming (NMR).
Evaluating generative audio systems and their metrics
Recent years have seen considerable advances in audio synthesis with deep generative models.
Representation Learning for the Automatic Indexing of Sound Effects Libraries
Labeling and maintaining a commercial sound effects library is a time-consuming task exacerbated by databases that continually grow in size and undergo taxonomy updates.
Feature-informed Embedding Space Regularization For Audio Classification
The workload is kept low during inference as the pre-trained features are only necessary for training.
Scream Detection in Heavy Metal Music
Harsh vocal effects such as screams or growls are far more common in heavy metal vocals than the traditionally sung vocal.
Feature-informed Latent Space Regularization for Music Source Separation
The integration of additional side information to improve music source separation has been investigated numerous times, e. g., by adding features to the input or by adding learning targets in a multi-task learning scenario.
Latte: Cross-framework Python Package for Evaluation of Latent-Based Generative Models
Latte (for LATent Tensor Evaluation) is a Python library for evaluation of latent-based generative models in the fields of disentanglement learning and controllable generation.
Evaluation of Latent Space Disentanglement in the Presence of Interdependent Attributes
Controllable music generation with deep generative models has become increasingly reliant on disentanglement learning techniques.
Improving Music Performance Assessment with Contrastive Learning
Several automatic approaches for objective music performance assessment (MPA) have been proposed in the past, however, existing systems are not yet capable of reliably predicting ratings with the same accuracy as professional judges.
Is Disentanglement enough? On Latent Representations for Controllable Music Generation
The structure of the latent space with respect to the VAE-decoder plays an important role in boosting the ability of a generative model to manipulate different attributes.
Mind the beat: detecting audio onsets from EEG recordings of music listening
We propose a deep learning approach to predicting audio event onsets in electroencephalogram (EEG) recorded from users as they listen to music.
Audio Content Analysis
With a focus on Music Information Retrieval systems, this chapter defines musical audio content, introduces the general process of audio content analysis, and surveys basic approaches to audio content analysis.
Melody-Conditioned Lyrics Generation with SeqGANs
Automatic lyrics generation has received attention from both music and AI communities for years.
Multitask learning for instrument activation aware music source separation
Music source separation is a core task in music information retrieval which has seen a dramatic improvement in the past years.
Score-informed Networks for Music Performance Assessment
The assessment of music performances in most cases takes into account the underlying musical score being performed.
dMelodies: A Music Dataset for Disentanglement Learning
In this paper, we present a new symbolic music dataset that will help researchers working on disentanglement problems demonstrate the efficacy of their algorithms on diverse domains.
Visual Attention for Musical Instrument Recognition
In the field of music information retrieval, the task of simultaneously identifying the presence or absence of multiple musical instruments in a polyphonic recording remains a hard problem.
Attribute-based Regularization of Latent Spaces for Variational Auto-Encoders
Selective manipulation of data attributes using deep generative models is an active area of research.
Explicitly Conditioned Melody Generation: A Case Study with Interdependent RNNs
In this study, we investigate the effects of explicitly conditioning deep generative models with musically relevant information.
An Attention Mechanism for Musical Instrument Recognition
While the automatic recognition of musical instruments has seen significant progress, the task is still considered hard for music featuring multiple instruments as opposed to single instrument recordings.
Learning to Traverse Latent Spaces for Musical Score Inpainting
The designed model takes both past and future musical context into account and is capable of suggesting ways to connect them in a musically meaningful manner.
Music Performance Analysis: A Survey
Music Information Retrieval (MIR) tends to focus on the analysis of audio signals.
Learning to Fuse Music Genres with Generative Adversarial Dual Learning
FusionGAN is a novel genre fusion framework for music generation that integrates the strengths of generative adversarial networks and dual learning.
