Automatic Music Analysis

Tabbed contents

Research

Understanding Music

How do I quickly find a suitable music piece in a large music catalog? Can I automatically receive recommendations for the perfect beat that harmonizes well with a music production I'm currently working on? Which programs in my archive are the most successful? These are typical questions where our technologies for automatic music analysis can help.

Audio signal processing and machine learning have fundamentally changed music analysis. The multidisciplinary research field "Music Information Retrieval" encompasses algorithms and techniques for extracting musical information from audio data, transforming it into interpretable formats. The results are applied in areas such as broadcast monitoring, music search and recommendation, music production, content tracking, and music eductaion.

AI-based music analysis technologies

General challenges in automatic music analysis include processing large amounts of data, considering musical diversity and context, robustness to variations in recording quality, and the efficient deployment of real-time processing for various applications.

Audio-Matching

Audio matching via audio fingerprinting enables the identification of specific audio recordings in music collections and streams. Media content is compared and matched based on acoustic fingerprints. Audio matching is used for analyzing music usage in broadcast monitoring, content tracking applications, archive maintenance, as well as in music search engines and recommendation systems.

At Fraunhofer IDMT, we research how to further improve the accuracy and efficiency of audio matching techniques in order to enable more precise detection and identification of media content.

Annotation and similarity search for music

Annotation and similarity search for music facilitate the organization of music collections and simplify access to musical content. The use of metadata allows for versatile search and recommendation systems, automating the discovery of suitable music or musical elements. This is applicable, for example, in end-user streaming services or music production.

We are working on enhancing annotation and similarity search, particularly for large and diverse music collections, while also considering to user preferences and contextual information.

Automatic music transcription

Automatic music transcription involves converting music signals into symbolic music notation and extracting musical structures such as melodies, chords, and rhythms. These techniques are used in music learning programs, music game development, and music theoretical studies.

The specific challenges of automatic music transcription lie in precisely, reliably, and real-time capturing complex musical structures, even in polyphonic musical pieces or situations with background and ambient noise.

Automatic music generation

Automatic music generation involves the development of algorithms and AI systems capable of creating their own original musical pieces or parts thereof. It provides automated support in the music production process and during live performances, for instance, by generating melodies based on harmonies. This emerging field introduces new creative approaches to music composition and production.

However, automatic music generation is still a relatively young research field and requires further progress to produce realistic and coherent musical results that meet the expectations of music creators and listeners. At Fraunhofer IDMT, we are researching ways to make the AI composition process transparent and controllable. Our aim is to support the creative collaboration between music creators and AI.

Projects and activities

Research project

Music Automaton (Musik-Automat)

Development of an AI-based composition app

Musik-Automat

Research project

ISAD 2

Develop explainable and comprehensible deep learning models to better understand sound source characteristics of music, environmental and ambient sounds

ISAD 2

Research project

AI4Media

Center of excellence for AI in media – Our contributions: Audio forensics, audio provenance analysis, music analysis, privacy and recommendation systems

AI4Media

Reference project

Jamahook – AI Sound Matching

Search engine for loops and beats based on SoundsLike

Jamahook

Reference project

SWR Media Services

Audio matching software for automatic advertising monitoring of SWR radio programs

SWR Media Services

Research project

MusicBricks

Musical Building Blocks for Digital Makers and Content Creators: Transfer state-of-the-art ICT to Creative SMEs in order to develop novel business models.

MusicBricks

Research project

SyncGlobal

Global music search applied to cross-modal synchronization with video content

syncglobal

Research project

GlobalMusic2one

Adaptive, hybrid search technologies for global music portfolios

GlobalMusic2one

Research project

MuSEc

Audio analysis and PET for the MusicDNA sustainable eco system

Research project

Emused

Interactive app for learning how to improvise on a musical instrument

emused

Research project

MiCO

Platform for multimodal and context-based analysis, into which a wide variety of analysis components for different media types can be integrated

MICO

Range of services

Products

SoundsLike

AI-based Tagging and Search for Large Music Catalogs

Soundslike

Audio Matching

Detect a given audio query within a stream or file – even under noisy conditions or with a very short query

Audio Matching

Speech and Music Detector

Software tool for automatic detection of music and speech sequences to optimize broadcasting programs or provide accurate accounting for copyright agencies

Speech and Music Detector

Automatic Music Transcription

Convert musical signals into notes for music games and music learning programs

Publications

Jahr Year	Titel/Autor:in Title/Author	Publikationstyp Publication Type
2024	Detecting chord tone alterations and suspensions McLeod, Andrew; Rohrmeier, Martin Alois	Zeitschriftenaufsatz Journal Article
2024	Aktuelle Forschungsschwerpunkte in der akustischen Ereignisdetektion Abeßer, Jakob; Grollmisch, Sascha; Bös, Joachim	Konferenzbeitrag Conference Paper
2023	Uncertainty in Semi-Supervised Audio Classification - A Novel Extension for FixMatch Grollmisch, Sascha; Cano, Estefanía; Lukashevich, Hanna; Abeßer, Jakob	Konferenzbeitrag Conference Paper
2023	Introducing DiMCAT for processing and analyzing notated music on a very large scale Hentschel, Johannes; McLeod, Andrew; Rammos, Yannis; Rohrmeier, Martin Alois	Konferenzbeitrag Conference Paper
2023	An Analysis of Automatically Generated Music McLeod, Andrew	Konferenzbeitrag Conference Paper
2023	Automatic Note-Level Score-to-Performance Alignments in the ASAP Dataset Peter, Silvan David; Cancino-Chacón, Carlos Eduardo; Foscarin, Francesco; McLeod, Andrew; Henkel, Florian; Karystinaios, Emmanouil; Widmer, Gerhard	Zeitschriftenaufsatz Journal Article
2022	Multi-input Architecture and Disentangled Representation Learning for Multi-dimensional Modeling of Music Similarity Ribecky, Sebastian; Abeßer, Jakob; Lukashevich, Hanna	Zeitschriftenaufsatz Journal Article
2022	Towards Interpreting and Improving the Latent Space for Musical Chord Recognition Nadar, Christon-Ragavan; Taenzer, Michael; Abeßer, Jakob	Konferenzbeitrag Conference Paper
2021	A Benchmark Dataset to Study Microphone Mismatch Conditions for Piano Multipitch Estimation on Mobile Devices Abeßer, Jakob; Bittner, Franca; Richter, Maike; Gonzalez Rodriguez, Marcel; Lukashevich, Hanna	Konferenzbeitrag Conference Paper
2021	Improving Semi-Supervised Learning for Audio Classification with FixMatch Grollmisch, Sascha; Cano, Estefanía	Zeitschriftenaufsatz Journal Article
2021	Ensemble Size Classification in Colombian Andean String Music Recordings Grollmisch, S.; Cano, E.; Mora Ángel, F.; López Gil, G.	Konferenzbeitrag Conference Paper
2021	Jazz Bass Transcription Using a U-Net Architecture Abeßer, J.; Müller, M.	Zeitschriftenaufsatz Journal Article
2021	Predominant Jazz Instrument Recognition. Empirical Studies on Neural Network Architectures Mimilakis, Stylianos I.; Abeßer, Jakob; Chauhan, Jaydeep; Pillai, Prateek Pradeep; Taenzer, Michael	Konferenzbeitrag Conference Paper
2021	A Novel Dataset for Time-Dependent Harmonic Similarity between Chord Sequences Bittner, Franca; Abeßer, Jakob; Nadar, Christon-Ragavan; Lukashevich, Hanna; Kramer, Patrick	Vortrag Presentation
2021	Towards Deep Learning Strategies for Transcribing Electroacoustic Music Abeßer, J.; Nowakowski, M.; Weiß, C.	Konferenzbeitrag Conference Paper
2020	Cross-Version Singing Voice Detection in Opera Recordings: Challenges for Supervised Learning Mimilakis, Stylianos Ioannis; Weiss, Christof; Arifi-Müller, Vlora; Abeßer, Jakob; Müller, Meinard	Konferenzbeitrag Conference Paper
2019	Ensemble size classification in Colombian Andean string music recordings Grollmisch, Sascha; Cano, Estefanía; Mora-Ãngel, Fernando; López Gil, Gustavo A.	Konferenzbeitrag Conference Paper
2019	Musical Source Separation Cano, E.; FitzGerald, D.; Liutkus, A.; Plumbley, M.D.; Stöter, F.-R.	Zeitschriftenaufsatz Journal Article
2019	Automatic Chord Recognition in Music Education Applications Grollmisch, Sascha; Cano, Estefanía	Konferenzbeitrag Conference Paper
2019	ACMUS-MIR: A new annotated data set of Andean Colombian music Mora-Ángel, Fernando; López Gil, Gustavo A.; Cano, Estefanía; Grollmisch, Sascha	Konferenzbeitrag Conference Paper
2019	Investigating CNN-based Instrument Family Recognition for Western Classical Music Recordings Mimilakis, Stylianos I.; Taenzer, Michael; Abeßer, Jakob; Weiss, Christof; Müller, Meinard; Lukashevich, Hanna	Konferenzbeitrag Conference Paper
2019	Towards CNN-based Acoustic Modeling of Seventh Chords for Automatic Chord Recognition Nadar, Christon-Ragavan; Abeßer, Jakob; Grollmisch, Sascha	Konferenzbeitrag Conference Paper
2019	Analysis and Visualisation of Music Wunsche, B.C.; Müller, S.; Tänzer, M.	Konferenzbeitrag Conference Paper
2018	MaD TwinNet: Masker-Denoiser Architecture with Twin Networks for Monaural Sound Source Separation Drossos, K.; Serdyuk, D.; Virtanen, T.; Bengio, Y.; Mimilakis, S.I.; Schuller, G.	Konferenzbeitrag Conference Paper
2018	Music Technology and Education Cano, E.; Dittmar, C.; Abeßer, J.; Kehling, C.; Grollmisch, S.	Aufsatz in Buch Book Article
2018	Improving Bass Saliency Estimation using Label Propagation and Transfer Learning Abeßer, Jakob; Balke, Stefan; Müller, Meinard	Konferenzbeitrag Conference Paper
2018	Computational Corpus Analysis: A Case Study on Jazz Solos Weiß, Christof; Balke, Stefan; Abeßer, Jakob; Müller, Meinard	Konferenzbeitrag Conference Paper
2018	Jazz Solo Instrument Classification with Convolutional Neural Networks, Source Separation, and Transfer Learning Gomez, Juan S.; Abeßer, Jakob; Cano, Estefanía	Konferenzbeitrag Conference Paper
2018	Reducing interference with phase recovery in DNN-based monaural singing voice separation Mimilakis, S.I.; Magron, P.; Drossos, K.; Virtanen, T.	Konferenzbeitrag Conference Paper
2018	The dimensions of perceptual quality of sound source separation Cano, Estefanía; Liebetrau, Judith; Fitzgerald, Derry; Brandenburg, Karlheinz	Konferenzbeitrag Conference Paper
2018	Harmonic-percussive source separation with deep neural networks and phase recovery Mimilakis, S.I.; Drossos, K.; Magron, P.; Virtanen, T.	Konferenzbeitrag Conference Paper
2017	Automatic speech/music discrimination for broadcast signals Kruspe, Anna M.; Zapf, Dominik; Lukashevich, Hanna	Konferenzbeitrag Conference Paper
2017	Computational methods for tonality-based style analysis of classical music audio recordings Weiß, Christof	Dissertation Doctoral Thesis
2017	Data-driven solo voice enhancement for jazz music retrieval Balke, Stefan; Dittmar, Christian; Abeßer, Jakob; Müller, Meinard	Konferenzbeitrag Conference Paper
2017	Soundslike - automatic content-based music annotation and recommendation for large databases Grollmisch, Sascha; Lukashevich, Hanna	Konferenzbeitrag Conference Paper
2017	Exploring sound source separation for acoustic condition monitoring in industrial scenarios Cano, Estefanía; Nowak, Johannes; Grollmisch, Sascha	Konferenzbeitrag Conference Paper
2017	Instrument-centered music transcription of solo bass guitar recordings Abeßer, Jakob; Schuller, Gerald	Zeitschriftenaufsatz Journal Article
2017	A recurrent encoder-decoder approach with skip-filtering connections for monaural singing voice separation Mimilakis, S.I.; Drossos, K.; Virtanen, T.; Schuller, G.	Konferenzbeitrag Conference Paper
2017	Deep learning for jazz walking bass transcription Abeßer, Jakob; Balke, Stefan; Frieler, Klaus; Pfleiderer, Martin; Müller, Meinard	Konferenzbeitrag Conference Paper
2017	Score-informed analysis of tuning, intonation, pitch modulation, and dynamics in jazz solos Abeßer, Jakob; Frieler, Klaus; Cano, Estefanía; Pfleiderer, Martin; Zaddach, Wolf-Georg	Zeitschriftenaufsatz Journal Article
2016	Bootstrapping a system for phoneme recognition and keyword spotting in unaccompanied singing Kruspe, Anna M.	Konferenzbeitrag Conference Paper
2016	Towards evaluating multiple predominant melody annotations in jazz recordings Balke, Stefan; Driedger, Jonathan; Abeßer, Jakob; Dittmar, Christian; Müller, Meinard	Konferenzbeitrag Conference Paper
2016	New sonorities for jazz recordings: Separation and mixing using deep neural networks Cano, Estefanía; Abeßer, Jakob; Schuller, Gerald; Mimilakis, Stylianos-Ioannis	Konferenzbeitrag Conference Paper
2016	Automatic best take detection for electric guitar and vocal studio recordings Bönsel, Carsten; Abeßer, Jakob; Grollmisch, Sascha; Mimilakis, Stylianos-Ioannis	Konferenzbeitrag Conference Paper
2016	Retrieval of textual song lyrics from sung inputs Kruspe, Anna M.	Konferenzbeitrag Conference Paper

Diese Liste ist ein Auszug aus der Publikationsplattform Fraunhofer-Publica

This list has been generated from the publication platform Fraunhofer-Publica

Audio signal processing and machine learning for music analysis

Overview

News and upcoming events

Advertising monitoring for SWR radio programs

DataTech 2024

A musical question-and-answer game with AI

Tabbed contents

Research

Understanding Music

AI-based music analysis technologies

Audio-Matching

Annotation and similarity search for music

Automatic music transcription

Automatic music generation

Projects and activities

Music Automaton (Musik-Automat)

ISAD 2

AI4Media

Jamahook – AI Sound Matching

SWR Media Services

MusicBricks

SyncGlobal

GlobalMusic2one

MuSEc

Emused

MiCO

Range of services

Products

SoundsLike

Audio Matching

Speech and Music Detector

Automatic Music Transcription

Publications

Datasets

Contact Press / Media

Hanna Lukashevich