This book provides quick access to different analysis algorithms and allows comparison between different approaches to the same task, making it useful for newcomers to audio signal processing and industry experts alike.
The design, implementation, and evaluation of a system for automatic audio signal classification is presented, differentiating between three speech classes, 13 musical genres, and background noise according to audio type.
A set of simple musically informed objective metrics enabling an objective and reproducible way of evaluating and comparing the output of music generative systems is proposed and demonstrated with several experiments on real-world data.
A system for the automatic classification of audio signals according to audio category is presented, which is motivated by the numerous advantages of such a tree-like structure, which include easy expansion capabilities, flexibility in the design of genre-dependent features and the ability to reduce the probability of costly errors.
The proposed attention model is compared to multiple models which include a baseline binary relevance random forest, recurrent neural network, and fully connected neural networks to show that incorporating attention leads to an overall improvement in classification accuracy metrics across all 20 instruments in the OpenMIC dataset.
This paper presents a comprehensive review of ADT research, including a thorough discussion of the task-specific challenges, categorization of existing techniques, and evaluation of several state-of-the-art systems.
A drum transcription algorithm using partially fixed non-negative matrix factorization is presented, which allows users to identify percussive events in complex mixtures with a minimal training set.
A novel deep learning-based approach for musical score inpainting which takes both past and future musical context into account and is capable of suggesting ways to connect them in a musically meaningful manner, demonstrates the merit of learning complex trajectories in the latent spaces of deep generative models.
A new method for the objective evaluation of audio-to-audio alignment systems is proposed that enables the use of arbitrary kinds of music as ground truth data and showed that the feature weighting algorithm could improve the alignment accuracies compared to the results of the individual features.
The learned features, obtained by a deep network in bottleneck architecture, give promising results and outperform state-of-the-art systems for audio chord detection.