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Found 10 papers, 8 papers with code
LLark: A Multimodal Instruction-Following Language Model for Music
Music has a unique and complex structure which is challenging for both expert humans and existing AI systems to understand, and presents unique challenges relative to other forms of audio.
Contrastive Learning-Based Audio to Lyrics Alignment for Multiple Languages
This way, we obtain a novel system that is simple to train end-to-end, can make use of weakly annotated training data, jointly learns a powerful text model, and is tailored to alignment.
Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling
In comparison to TCN and Wavenet, our network consistently saves memory and computation time, with speed-ups for training and inference of over 4x in the audio generation experiment in particular, while achieving a comparable performance in all tasks.
Ranked #2 on
Music Modeling
on Nottingham
Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators
We apply our method to image generation, image segmentation and audio source separation, and obtain improved performance over a standard GAN when additional incomplete training examples are available.
GAN-based Generation and Automatic Selection of Explanations for Neural Networks
However, this requires a careful selection of hyper-parameters to generate interpretable examples for each neuron of interest, and current methods rely on a manual, qualitative evaluation of each setting, which is prohibitively slow.
Ensemble Models for Spoofing Detection in Automatic Speaker Verification
Our ensemble model outperforms all our single models and the baselines from the challenge for both attack types.
Audio and Speech Processing Sound
End-to-end Lyrics Alignment for Polyphonic Music Using an Audio-to-Character Recognition Model
Time-aligned lyrics can enrich the music listening experience by enabling karaoke, text-based song retrieval and intra-song navigation, and other applications.
Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation
Models for audio source separation usually operate on the magnitude spectrum, which ignores phase information and makes separation performance dependant on hyper-parameters for the spectral front-end.
Ranked #26 on
Music Source Separation
on MUSDB18
Jointly Detecting and Separating Singing Voice: A Multi-Task Approach
A main challenge in applying deep learning to music processing is the availability of training data.
Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice Extraction
Based on this idea, we drive the separator towards outputs deemed as realistic by discriminator networks that are trained to tell apart real from separator samples.
