In this paper, we trace the evolution of Music Information Retrieval (MIR) over the past 25 years. While MIR gathers all kinds of research related to music informatics, a large part of it focuses on signal processing techniques for music data, fostering a close relationship with the IEEE Audio and Acoustic Signal Processing Technical Commitee. In this paper, we reflect the main research achievements of MIR along the three EDICS related to music analysis, processing and generation. We then review a set of successful practices that fuel the rapid development of MIR research. One practice is the annual research benchmark, the Music Information Retrieval Evaluation eXchange, where participants compete on a set of research tasks. Another practice is the pursuit of reproducible and open research. The active engagement with industry research and products is another key factor for achieving large societal impacts and motivating younger generations of students to join the field. Last but not the least, the commitment to diversity, equity and inclusion ensures MIR to be a vibrant and open community where various ideas, methodologies, and career pathways collide. We finish by providing future challenges MIR will have to face.

Popular music is often composed of an accompaniment and a lead component, the latter typically consisting of vocals. Filtering such mixtures to extract one or both components has many applications, such as automatic karaoke and remixing. This particular case of source separation yields very specific challenges and opportunities, including the particular complexity of musical structures, but also relevant prior knowledge coming from acoustics, musicology or sound engineering. Due to both its importance in applications and its challenging difficulty, lead and accompaniment separation has been a popular topic in signal processing for decades. In this article, we provide a comprehensive review of this research topic, organizing the different approaches according to whether they are model-based or data-centered. For model-based methods, we organize them according to whether they concentrate on the lead signal, the accompaniment, or both. For data-centered approaches, we discuss the particular difficulty of obtaining data for learning lead separation systems, and then review recent approaches, notably those based on deep learning. Finally, we discuss the delicate problem of evaluating the quality of music separation through adequate metrics and present the results of the largest evaluation, to-date, of lead and accompaniment separation systems. In conjunction with the above, a comprehensive list of references is provided, along with relevant pointers to available implementations and repositories.

In this paper, we propose a simple user-assisted method for the recovery of repeating patterns in time and frequency which can occur in audio mixtures. Here, the user selects a region in a log-frequency spectrogram from which they seek to recover the underlying pattern, such as a repeating chord masked by a cough. Cross-correlation is then performed between the selected region and the spectrogram, revealing similar regions. The most similar region is then selected and a variant on the PROJET algorithm, termed PROJET-MAG, is used to extract the common time-frequency components from the two regions, as well as extracting the components which are not common. The results obtained are compared to another user-assisted method based on REPET, and PROJET-MAG is demonstrated to give improved results over this baseline.

We approach cover song identification using a novel time-series representation of audio based on the 2DFT. The audio is represented as a sequence of magnitude 2D Fourier Transforms (2DFT). This representation is robust to key changes, timbral changes, and small local tempo deviations. We look at cross-similarity between these time-series, and extract a distance measure that is invariant to music structure changes. Our approach is state-of-the-art on a recent cover song dataset, and expands on previous work using the 2DFT for music representation and work on live song recognition.

Recently, Kernel Additive Modelling (KAM) was proposed as a unified framework to achieve multichannel audio source separation. Its main feature is to use kernel models for locally describing the spectrograms of the sources. Such kernels can capture source features such as repetitivity, stability over time and/or frequency, self-similarity, etc. KAM notably subsumes many popular and effective methods from the state of the art, including REPET and harmonic/percussive separation with median filters. However, it also comes with an important drawback in its initial form: its memory usage badly scales with the number of sources. Indeed, KAM requires the storage of the full-resolution spectrogram for each source, which may become prohibitive for full-length tracks or many sources. In this paper, we show how it can be combined with a fast compression algorithm of its parameters to address the scalability issue, thus enabling its use on small platforms or mobile devices.

Repetition is a fundamental element in generating and perceiving structure in audio. Especially in music, structures tend to be composed of patterns that repeat through time (e.g., rhythmic elements in a musical accompaniment), and also frequency (e.g., different notes of the same instrument). The auditory system has the remarkable ability to parse such patterns by identifying repetitions within the audio mixture. On this basis, we propose a simple user interface system for recovering patterns repeating in time and frequency in mixtures of sounds. A user selects a region in the log-frequency spectrogram of an audio recording from which she/he wishes to recover a repeating pattern masked by an undesired element (e.g., a note masked by a cough). The selected region is then cross-correlated with the spectrogram to identify similar regions where the underlying pattern repeats. The identified regions are finally averaged over their repetitions and the repeating pattern is recovered.

Suppose that you are at a music festival checking on an artist, and you would like to quickly know about the song that is being played (e.g., title, lyrics, album, etc.). If you have a smartphone, you could record a sample of the live performance and compare it against a database of existing recordings from the artist. Services such as Shazam or SoundHound will not work here, as this is not the typical framework for audio fingerprinting or query-by-humming systems, as a live performance is neither identical to its studio version (e.g., variations in instrumentation, key, tempo, etc.) nor it is a hummed or sung melody. We propose an audio fingerprinting system that can deal with live version identification by using image processing techniques. Compact fingerprints are derived using a log-frequency spectrogram and an adaptive thresholding method, and template matching is performed using the Hamming similarity and the Hough Transform.

Musical works are often composed of two characteristic components: the background (typically the musical accompaniment), which generally exhibits a strong rhythmic structure with distinctive repeating time elements, and the melody (typically the singing voice or a solo instrument), which generally exhibits a strong harmonic structure with a distinctive predominant pitch contour. Drawing from findings in cognitive psychology, we propose to investigate the simple combination of two dedicated approaches for separating those two components: a rhythm-based method that focuses on extracting the background via a rhythmic mask derived from identifying the repeating time elements in the mixture and a pitch-based method that focuses on extracting the melody via a harmonic mask derived from identifying the predominant pitch contour in the mixture. Evaluation on a data set of song clips showed that combining such two contrasting yet complementary methods can help to improve separation performance-from the point of view of both components-compared with using only one of those methods, and also compared with two other state-of-the-art approaches.

REPET-SIM is a generalization of the REpeating Pattern Extraction Technique (REPET) that uses a similarity matrix to separate the repeating background from the non-repeating foreground in a mixture. The method assumes that the background (typically the music accompaniment) is dense and low-ranked, while the foreground (typically the singing voice) is sparse and varied. While this assumption is often true for background music and foreground voice in musical mixtures, it also often holds for background noise and foreground speech in noisy mixtures. We therefore propose here to extend REPET-SIM for noise/speech segregation. In particular, given the low computational complexity of the algorithm, we show that the method can be easily implemented online for real-time processing. Evaluation on a data set of 10 stereo two-channel mixtures of speech and real-world background noise showed that this online REPET-SIM can be successfully applied for real-time speech enhancement, performing as well as different competitive methods.

Repetition is a core principle in music. Many musical pieces are characterized by an underlying repeating structure over which varying elements are superimposed. This is especially true for pop songs where a singer often overlays varying vocals on a repeating accompaniment. On this basis, we present the REpeating Pattern Extraction Technique (REPET), a novel and simple approach for separating the repeating “background” from the non-repeating “foreground” in a mixture. The basic idea is to identify the periodically repeating segments in the audio, compare them to a repeating segment model derived from them, and extract the repeating patterns via time-frequency masking. Experiments on data sets of 1,000 song clips and 14 full-track real-world songs showed that this method can be successfully applied for music/voice separation, competing with two recent state-of-the-art approaches. Further experiments showed that REPET can also be used as a preprocessor to pitch detection algorithms to improve melody extraction.

The separation of the lead vocals from the background accompaniment in audio recordings is a challenging task. Recently, an efficient method called REPET (REpeating Pattern Extraction Technique) has been proposed to extract the repeating background from the non-repeating foreground. While effective on individual sections of a song, REPET does not allow for variations in the background (e.g. verse vs. chorus), and is thus limited to short excerpts only. We overcome this limitation and generalize REPET to permit the processing of complete musical tracks. The proposed algorithm tracks the period of the repeating structure and computes local estimates of the background pattern. Separation is performed by soft time-frequency masking, based on the deviation between the current observation and the estimated background pattern. Evaluation on a dataset of 14 complete tracks shows that this method can perform at least as well as a recent competitive music/voice separation method, while being computationally efficient.

The Degenerate Unmixing Estimation Technique (DUET) is a Blind Source Separation (BSS) algorithm for stereo audio. DUET depends on an amplitude-phase 2d histogram built from the differences between the two channels, where peaks in the histogram indicate sources in the mixture. If peaks overlap, separation becomes unfeasible. This is often the case for music mixtures. We propose to improve peak separation by building histograms from time-frequency representations based on the Constant Q Transform (CQT) instead of the Fourier Transform (FT). The CQT has a logarithmic frequency resolution matching the geometrically spaced notes of the Western music scale. We also adaptively resize histogram bins and use Wiener filtering to improve peak resolving and source reconstruction. Results on mixtures of harmonic musical instruments show improvement in separation, especially at low frequencies and for closely spaced sources.

Repetition is a core principle in music. This is especially true for popular songs, generally marked by a noticeable repeating musical structure, over which the singer performs varying lyrics. On this basis, we propose a simple method for separating music and voice, by extraction of the repeating musical structure. First, the period of the repeating structure is found. Then, the spectrogram is segmented at period boundaries and the segments are averaged to create a repeating segment model. Finally, each time-frequency bin in a segment is compared to the model, and the mixture is partitioned using binary time-frequency masking by labeling bins similar to the model as the repeating background. Evaluation on a dataset of 1,000 song clips showed that this method can improve on the performance of an existing music/voice separation method without requiring particular features or complex frameworks.