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Found 13 papers, 4 papers with code
FreGrad: Lightweight and Fast Frequency-aware Diffusion Vocoder
The goal of this paper is to generate realistic audio with a lightweight and fast diffusion-based vocoder, named FreGrad.
Similar but Faster: Manipulation of Tempo in Music Audio Embeddings for Tempo Prediction and Search
We propose tempo translation functions that allow for efficient manipulation of tempo within a pre-existing embedding space whilst maintaining other properties such as genre.
Tempo estimation as fully self-supervised binary classification
This paper addresses the problem of global tempo estimation in musical audio.
On the Effect of Data-Augmentation on Local Embedding Properties in the Contrastive Learning of Music Audio Representations
Similarly, we show that the optimal selection of data augmentation strategies for contrastive learning of music audio embeddings is dependent on the downstream task, highlighting this as an important embedding design decision.
Seeing Through the Conversation: Audio-Visual Speech Separation based on Diffusion Model
For an effective fusion of the two modalities for diffusion, we also propose a cross-attention-based feature fusion mechanism.
Let There Be Sound: Reconstructing High Quality Speech from Silent Videos
In this paper, we propose a novel lip-to-speech system that significantly improves the generation quality by alleviating the one-to-many mapping problem from multiple perspectives.
Contrastive Learning for Cross-modal Artist Retrieval
We demonstrate our method successfully combines complementary information from diverse modalities, and is more robust to missing modality data (i. e., it better handles the retrieval of artists with different modality embeddings than the query artist's).
Generative Autoregressive Networks for 3D Dancing Move Synthesis from Music
The trained dance pose generator, which is a generative autoregressive model, is able to synthesize a dance sequence longer than 5, 000 pose frames.
Are Nearby Neighbors Relatives?: Testing Deep Music Embeddings
The underlying assumption is that in case a deep representation is to be trusted, distance consistency between known related points should be maintained both in the input audio space and corresponding latent deep space.
Effective Network Compression Using Simulation-Guided Iterative Pruning
Existing high-performance deep learning models require very intensive computing.
Transfer Learning of Artist Group Factors to Musical Genre Classification
The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy.
One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies
In this paper, we present the results of our investigation of what are the most important factors to generate deep representations for the data and learning tasks in the music domain.
Deep convolutional neural networks for predominant instrument recognition in polyphonic music
We train our network from fixed-length music excerpts with a single-labeled predominant instrument and estimate an arbitrary number of predominant instruments from an audio signal with a variable length.
