Overview of AccoMontage: Combining Rule-Based Optimization and Deep Learning for Music Generation
AccoMontage is a model for accompaniment arrangement that generates piano accompaniments for folk/pop songs based on a lead sheet. This type of music generation task involves intertwined constraints of melody, harmony, texture, and music structure. AccoMontage is unique in that it combines rule-based optimization and deep learning, rather than relying on just one method. This hybrid pathway approac
What is COLA?
COLA stands for “Contrastive Learning of Audio”. It is a method used to train artificial intelligence models to learn a general-purpose representation of audio. Essentially, COLA helps machines understand what different sounds mean.
How Does COLA Work?
The COLA model learns by contrasting similarities and differences within audio segments. It assigns a high level of similarity to segments extracted from the same recording, while labeling segments from different recordings as le
Overview of CTAL: Pre-Training Framework for Audio-and-Language Representations
CTAL is a pre-training framework for creating strong audio-and-language representations with a Transformer. In simpler terms, it helps computers understand the relationship between spoken language and written language.
How does CTAL work?
CTAL accomplishes its goal through two types of tasks that it performs on a large number of audio and language pairs: masked language modeling and masked cross-modal acoustic mo
HiFi-GAN: A Deep Learning Model for Speech Synthesis
In recent years, deep learning has shown promising results in numerous areas of research. One area that has seen tremendous improvement is speech synthesis. HiFi-GAN, short for High Fidelity Generative Adversarial Network, is one such deep learning model that generates high-quality speech. In this article, we will explore how HiFi-GAN works and its impact on speech synthesis.
How Does HiFi-GAN Work?
HiFi-GAN is a type of generative adversa
Jukebox: Generating Music with Singing in Raw Audio Domain
If you are a fan of music, you might be interested in a new model that generates music with singing in the raw audio domain. It's called Jukebox. The model is designed to tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. It can condition on artist and genre to steer the musical and vocal style and on unaligned lyrics to make the singing
MelGAN is an exciting development in audio waveform generation using a GAN setup. It is a fully convolutional feed-forward network that takes a mel-spectrogram as input and outputs raw waveform.
What is Mel-spectrogram?
A mel-spectrogram represents the frequency content of a signal at different points in time. In other words, it is a visual representation of sound that shows how much energy is present in a particular frequency band at a particular time. The y-axis of a mel-spectrogram represe
Overview of Multi-Band MelGAN
Multi-band MelGAN, also known as MB-MelGAN, is an advanced waveform generation model that focuses on high-quality text-to-speech generation. MB-MelGAN improves upon the original MelGAN model by increasing the generator's receptive field and using a multi-resolution STFT loss instead of the feature matching loss to measure the difference between fake and real speech. Additionally, MB-MelGAN is extended with multi-band processing, allowing the generator to take mel-s
SpecGAN is a computational model designed to produce sound samples that mimic human-made sounds. This process is called generative audio, and it utilizes artificial intelligence to create complex sound samples. SpecGAN is made using generative adversarial network methods, which is a type of artificial neural network.
The Problem with Generating Audio Using GAN
GANs are a popular method used for image generation, but they aren't suitable for producing audio because of how complex sound waves a
VocGAN, short for Voice Generative Adversarial Network, is an artificial intelligence (AI) technology designed to generate realistic human-like speech. Developed by researchers at Microsoft, VocGAN is a type of deep learning model that uses a combination of generative and discriminative neural networks to produce high-quality speech from text inputs or audio recordings.
How Does VocGAN Work?
The primary purpose of VocGAN is to improve the accuracy and naturalness of Text-to-Speech (TTS) syste
WaveGAN: Generating Raw-Waveform Audio using GANs
WaveGAN is an exciting development in the field of machine learning that allows for the unsupervised synthesis of raw-waveform audio. It uses a type of neural network called a Generative Adversarial Network (GAN) to generate realistic audio waveforms that have never been heard before. WaveGAN's architecture is based on another type of GAN called DCGAN, but with certain modifications to make it better suited for audio generation.
How Does WaveG
WaveGlow: The Next Level of Audio Generation
Audio generation has come a long way over the years, thanks to the development of new technologies and techniques. One of the latest advancements in this field is WaveGlow, a flow-based generative model that can create high-quality audio by sampling from a distribution. The result is pristine, complex sound waves that sound like they were created by a human musician.
How WaveGlow Works
The concept behind WaveGlow is simple: you start with a simple
WaveGrad: A New Approach to Audio Waveform Generation
If you're a fan of music or podcasts, you may be familiar with the idea of audio waveform generation. This refers to the process of creating sound waves from scratch, like when musicians record music or voice actors record dialogue. Recently, a new method for generating audio waveforms has emerged called WaveGrad, which is creating quite a buzz in the tech world. Let's explore what WaveGrad is all about and how it works.
What is WaveGrad?
WaveNet is a type of audio generative model that is able to learn the patterns and structures within audio data to produce new audio samples. It is based on the PixelCNN architecture, which is a type of neural network that excels at image processing tasks, but has been adapted to work with audio data. WaveNet is designed to deal with long-range temporal dependencies, meaning it can recognize patterns that occur over long periods of time, such as a melody or a speech pattern.
How WaveNet Works
Introduction to WaveRNN
WaveRNN is a type of neural network that is used for generating audio. This network is designed to predict 16-bit raw audio samples with high efficiency. It is a single-layer recurrent neural network that consists of different computations, including sigmoid and tanh non-linearities, matrix-vector products, and softmax layers.
How WaveRNN Works
WaveRNN works by predicting audio samples from coarse and fine parts that are encoded as scalars in a range of 0 to 255. Thes
What is WaveVAE?
WaveVAE is a type of generative audio model that can be used to enhance text-to-speech systems. It uses a VAE-based model and can be trained from scratch by optimizing the encoder and decoder. The encoder represents the ground truth audio data as a latent representation, while the decoder predicts future audio frames
How Does WaveVAE Work?
WaveVAE uses a Gaussian autoregressive WaveNet for its encoder. This means that it maps the ground truth audio data into a latent represe
