Accelerating Index-Based Audio Identification - Schreiber, Hendrik; Müller, Meinard (IEEE Transactions on Multimedia, 2014)

A Crowdsourced Experiment for Tempo Estimation of Electronic Dance Music - Schreiber, Hendrik; Müller, Meinard (Proceedings of the International Conference on Music Information Retrieval (ISMIR), 2018)

A Post-Processing Procedure for Improving Music Tempo Estimates Using Supervised Learning - Schreiber, Hendrik; Müller, Meinard (Proceedings of the International Conference on Music Information Retrieval (ISMIR), 2017)

A Re-ordering Strategy for Accelerating Index-based Audio Fingerprinting - Schreiber, Hendrik; Grosche, Peter; Müller, Meinard (Proceedings of the International Conference on Music Information Retrieval (ISMIR), 2011)

A Single-Step Approach to Musical Tempo Estimation Using a Convolutional Neural Network - Schreiber, Hendrik; Müller, Meinard (Proceedings of the International Conference on Music Information Retrieval (ISMIR), 2018)

Exploiting Global Features for Tempo Octave Correction - Schreiber, Hendrik; Müller, Meinard (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2014)

Local Key Estimation In Classical Music Recordings: A Cross-Version Study on Schubert’s Winterreise - Schreiber, Hendrik; Weiß, Christof; Müller, Meinard (ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020)
While global key and chord estimation for both popular and classical music recordings have received a lot of attention, little research has been devoted to estimating the local key for classical music. In this work, we approach local key estimation on a unique cross-version dataset comprising nine performances (versions) of Schubert's song cycle Winterreise-a challenging scenario of high musical ambiguity and subjectivity. We compare an HMM-based system with a CNN-based approach. For both models, we employ a similar training procedure including the optimization of hyperparameters on a validation split. We systematically evaluate the model predictions and provide musical explanations for key confusions. As our main contribution, we explore how different training-test splits affect the models' efficacy. Splitting along the song axis, we find that both methods perform similarly well. Splitting along the version axis leads to clearly higher results, especially for the CNN, which seems to effectively learn the harmonic progressions of the songs ("cover song effect") and successfully generalizes to unseen versions.<br
Keywords: Training; Hidden Markov models; Estimation; Music; Training data; Harmonic analysis; Task analysis; music information retrieval; local key estimation; harmony analysis; evaluation; deep neural networks

Local Key Estimation in Music Recordings: A Case Study across Songs, Versions, and Annotators - Weiß, Christof; Schreiber, Hendrik; Müller, Meinard (IEEE/ACM Transactions on Audio, Speech and Language Processing, 2020)
While global key and chord estimation for both popular and classical music recordings have received a lot of attention, little research has been devoted to estimating the local key for classical music. Partly, this may be due to its inherent ambiguity and subjectivity, which makes annotating local keys a challenging task. In this article, we approach local key estimation with a cross-version dataset comprising nine performances (versions) of Schubert's song cycle Winterreise annotated by three different music theory experts. We consider two baseline methods that are representative for common types of signal processing algorithms: an HMM-based system and a CNN-based approach. For both models, we employ a similar training procedure including the optimization of hyperparameters on a validation split. We systematically evaluate the model predictions and provide musical explanations for key confusions. As a central contribution, we explore how different training-test splits affect the models' efficacy. Splitting along the song axis, we find that both methods perform similarly well. Splitting along the version axis, we obtain substantially higher accuracies, especially for the CNN, which seems to effectively learn the harmonic progressions of the songs ('cover song effect') and successfully generalizes to unseen versions. We further discuss the results for several songs in detail and assess our results from the perspective of multiple annotators. This cross-annotator study reveals that a substantial part of the systems' errors coincides with annotator disagreement and an even larger part can be traced back to musically explainable relationships among different keys.
CRIS-Team Scopus Importer:2020-11-27
Keywords: Deep neural networks; evaluation; harmony analysis; local key estimation; music information retrieval

Modeling and Estimating Local Tempo: A Case Study on Chopin's Mazurkas - Schreiber, Hendrik; Zalkow, Frank; Müller, Meinard (Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), 2020)

Musical Tempo and Key Estimation with Directional Convolutional Neural Networks - Schreiber, Hendrik; Müller, Meinard (Proceedings of the Sound and Music Computing Conference (SMC), 2019)

Music Tempo Estimation: Are We Done Yet? - Schreiber, Hendrik; Urbano, Julián; Müller, Meinard (Transactions of the International Society for Music Information Retrieval, 2020)
With the advent of deep learning, global tempo estimation accuracy has reached a new peak, which presents a great opportunity to evaluate our evaluation practices. In this article, we discuss presumed and actual applications, the pros and cons of commonly used metrics, and the suitability of popular datasets. To guide future research, we present results of a survey among domain experts that investigates today’s applications, their requirements, and the usefulness of currently employed metrics. To aid future evaluations, we present a public repository containing evaluation code as well as estimates by many different systems and different ground truths for popular datasets.<br
Keywords: Evaluation; Tempo Estimation; Metric; Dataset

Towards Automatically Correcting Tapped Beat Annotations for Music Recordings - Driedger, Jonathan; Schreiber, Hendrik; Haas, Bas; Müller, Meinard (Proceedings of the International Conference on Music Information Retrieval (ISMIR), 2019)