We provide a survey of the field of Music Information Retrieval (MIR), in particular paying
attention to latest developments, such as semantic auto-tagging and user-centric retrieval …

Time series classification is a subfield of machine learning with numerous real-life
applications. Due to the temporal structure of the input data, standard machine learning …

The Dynamic Time War** algorithm (DTW) is a well-known algorithm in many areas.
While first introduced in 60s [1] and extensively explored in 70s by application to the speech …

An Introduction to Audio Content Analysis Enables readers to understand the algorithmic
analysis of musical audio signals with AI-driven approaches An Introduction to Audio …

Music signal processing may appear to be the junior relation of the large and mature field of
speech signal processing, not least because many techniques and representations …

This paper forecasts the future spread of COVID-19 by exploiting the identified lead-lag
effects between different countries. Specifically, we first determine the past relation among …

Music can be described and represented in many different ways, including as sheet music,
symbolic representations, and audio recordings (Müller, 2015). For each of these …

Given a large audio database of music recordings, the goal of classical audio identification
is to identify a particular audio recording by means of a short audio fragment. Even though …

Dynamic Time War** (DTW) is an established method for finding a global alignment
between two feature sequences. However, having a computational complexity that is …

To overcome the computational complexity of the asynchronous hidden Markov model
(AHMM), we present a novel multidimensional dynamic time war** (DTW) algorithm for …