Segmentation process and spectral characteristics in the determination of musical genres

Over the past few years there has been a tendency to store audio tracks for later use on CD-DVDs, HDD-SSDs as well as on the internet, which makes it challenging to classify the information either online or offline. For this purpose, the audio tracks must be tagged. Tags are said to be texts based on the semantic information of the sound [1]. Thus, music analysis can be done in several ways [2] since music is identified by its genre, artist, instruments and structure, by a tagging system that can be manual or automatic. The manual tagging allows the visualization of the behavior of an audio track either in time domain or in frequency domain as in the spectrogram, making it possible to classify the songs without listening to them. However, this process is very time consuming and labor intensive, including health problems [3] which shows that "the volume, sound sensitivity, time and cost required for a manual labeling process is generally prohibitive. Three fundamental steps are required to carry out automatic labelling: pre-processing, feature extraction and classification [4]. The present study developed an algorithm for performing automatic classification of music genres using a segmentation process employing spectral characteristics such as centroid (SC), flatness (SF) and spread (SS), as well as a time spectral characteristic

AI and Tempo Estimation: A Review

The author's goal in this paper is to explore how artificial intelligence (AI) has been utilised to inform our understanding of and ability to estimate at scale a critical aspect of musical creativity - musical tempo. The central importance of tempo to musical creativity can be seen in how it is used to express specific emotions (Eerola and Vuoskoski 2013), suggest particular musical styles (Li and Chan 2011), influence perception of expression (Webster and Weir 2005) and mediate the urge to move one's body in time to the music (Burger et al. 2014). Traditional tempo estimation methods typically detect signal periodicities that reflect the underlying rhythmic structure of the music, often using some form of autocorrelation of the amplitude envelope (Lartillot and Toiviainen 2007). Recently, AI-based methods utilising convolutional or recurrent neural networks (CNNs, RNNs) on spectral representations of the audio signal have enjoyed significant improvements in accuracy (Aarabi and Peeters 2022). Common AI-based techniques include those based on probability (e.g., Bayesian approaches, hidden Markov models (HMM)), classification and statistical learning (e.g., support vector machines (SVM)), and artificial neural networks (ANNs) (e.g., self-organising maps (SOMs), CNNs, RNNs, deep learning (DL)). The aim here is to provide an overview of some of the more common AI-based tempo estimation algorithms and to shine a light on notable benefits and potential drawbacks of each. Limitations of AI in this field in general are also considered, as is the capacity for such methods to account for idiosyncrasies inherent in tempo perception, i.e., how well AI-based approaches are able to think and act like humans.Comment: 9 page

A post-processing procedure for improving music tempo estimates using supervised learning

Tempo estimation is a fundamental problem in music information retrieval and has been researched extensively. One problem still unsolved is the tendency of tempo estimation algorithms to produce results that are wrong by a small number of known factors (so-called octave errors). We propose a method that uses supervised learning to predict such tempo estimation errors. In a post-processing step, these predictions can then be used to correct an algorithm's tempo estimates. While being simple and relying only on a small number of features, our proposed method significantly increases accuracy for state-of-the-art tempo estimation methods