Search Results for author:
Found 28 papers, 14 papers with code
Music Enhancement with Deep Filters: A Technical Report for The ICASSP 2024 Cadenza Challenge
In this challenge, we disentangle the deep filters from the original DeepfilterNet and incorporate them into our Spec-UNet-based network to further improve a hybrid Demucs (hdemucs) based remixing pipeline.
Evaluating Co-Creativity using Total Information Flow
We propose a method to compute the information flow using pre-trained generative models as entropy estimators.
PosCUDA: Position based Convolution for Unlearnable Audio Datasets
Recent works such as CUDA propose solutions to this problem by adding class-wise blurs to make datasets unlearnable, i. e a model can never use the acquired dataset for learning.
Equipping Pretrained Unconditional Music Transformers with Instrument and Genre Controls
We then propose a simple technique to equip this pretrained unconditional music transformer model with instrument and genre controls by finetuning the model with additional control tokens.
SelfVC: Voice Conversion With Iterative Refinement using Self Transformations
In this work, instead of explicitly disentangling attributes with loss terms, we present a framework to train a controllable voice conversion model on entangled speech representations derived from self-supervised learning and speaker verification models.
Towards Improving Harmonic Sensitivity and Prediction Stability for Singing Melody Extraction
In deep learning research, many melody extraction models rely on redesigning neural network architectures to improve performance.
MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies
Diffusion models have shown promising results in cross-modal generation tasks, including text-to-image and text-to-audio generation.
Multitrack Music Transformer
Existing approaches for generating multitrack music with transformer models have been limited in terms of the number of instruments, the length of the music segments and slow inference.
TONet: Tone-Octave Network for Singing Melody Extraction from Polyphonic Music
In this paper, we propose TONet, a plug-and-play model that improves both tone and octave perceptions by leveraging a novel input representation and a novel network architecture.
HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection
To combat these problems, we introduce HTS-AT: an audio transformer with a hierarchical structure to reduce the model size and training time.
Ranked #4 on
Sound Event Detection
on DESED
Zero-shot Audio Source Separation through Query-based Learningfrom Weakly-labeled Data
Our approach uses a single model for source separation of multiple sound types, and relies solely on weakly-labeled data for training.
Zero-shot Audio Source Separation through Query-based Learning from Weakly-labeled Data
Our approach uses a single model for source separation of multiple sound types, and relies solely on weakly-labeled data for training.
Ranked #1 on
Audio Source Separation
on AudioSet
Towards Cross-Cultural Analysis using Music Information Dynamics
The framework is based on an Music Information Dynamics model, a Variable Markov Oracle (VMO), and is extended with a variational representation learning of audio.
Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition
We implement several multi-class classifiers with deep audio embeddings to predict emotion semantics in music.
Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets
Our approach generates metadata at the aggregator using the models received from clients and retrains the federated model to achieve bias-free results for image synthesis.
WaveGuard: Understanding and Mitigating Audio Adversarial Examples
There has been a recent surge in adversarial attacks on deep learning based automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Cross-modal Adversarial Reprogramming
Recent works on adversarial reprogramming have shown that it is possible to repurpose neural networks for alternate tasks without modifying the network architecture or parameters.
Deep Music Information Dynamics
In this paper we introduce a novel framework that we call Deep Musical Information Dynamics, which combines two parallel streams - a low rate latent representation stream that is assumed to capture the dynamics of a thought process contrasted with a higher rate information dynamics derived from the musical data itself.
Expressive Neural Voice Cloning
In this work, we propose a controllable voice cloning method that allows fine-grained control over various style aspects of the synthesized speech for an unseen speaker.
DPD-InfoGAN: Differentially Private Distributed InfoGAN
The Information Maximizing GAN (InfoGAN) is a variant of the default GAN that introduces feature-control variables that are automatically learned by the framework, hence providing greater control over the different kinds of images produced.
Music SketchNet: Controllable Music Generation via Factorized Representations of Pitch and Rhythm
Drawing an analogy with automatic image completion systems, we propose Music SketchNet, a neural network framework that allows users to specify partial musical ideas guiding automatic music generation.
Continuous Melody Generation via Disentangled Short-Term Representations and Structural Conditions
Automatic music generation is an interdisciplinary research topic that combines computational creativity and semantic analysis of music to create automatic machine improvisations.
Query-based Deep Improvisation
In this paper we explore techniques for generating new music using a Variational Autoencoder (VAE) neural network that was trained on a corpus of specific style.
Universal Adversarial Perturbations for Speech Recognition Systems
In this work, we demonstrate the existence of universal adversarial audio perturbations that cause mis-transcription of audio signals by automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Expediting TTS Synthesis with Adversarial Vocoding
Recent approaches in text-to-speech (TTS) synthesis employ neural network strategies to vocode perceptually-informed spectrogram representations directly into listenable waveforms.
The Effect of Explicit Structure Encoding of Deep Neural Networks for Symbolic Music Generation
With recent breakthroughs in artificial neural networks, deep generative models have become one of the leading techniques for computational creativity.
Rethinking Recurrent Latent Variable Model for Music Composition
Our results suggest that the proposed model has a better statistical resemblance to the musical structure of the training data, which improves the creation of new sequences of music in the style of the originals.
Adversarial Reprogramming of Text Classification Neural Networks
Adversarial Reprogramming has demonstrated success in utilizing pre-trained neural network classifiers for alternative classification tasks without modification to the original network.
