Alexander Lerch

Evaluation of Features for Audio-to-Audio Alignment

Abstract Audio-to-audio alignment is the task of synchronizing two audio sequences with similar musical content in time. We investigated a large set of audio features for this task. The features were chosen to represent four different content-dependent similarity categories: the envelope, the timbre, note-onsets and the pitch. The features were subjected to two processing stages. First, a feature subset was selected by evaluating the alignment performance of each individual feature. Second, the selected features were combined and subjected to an automatic weighting algorithm. A new method for the objective evaluation of audio-to-audio alignment systems is proposed that enables the use of arbitrary kinds of music as ground truth data. We evaluated our algorithm by this method as well as on a data set of real recordings of solo piano music. The results showed that the feature weighting algorithm could improve the alignment accuracies compared to the results of the individual features.

Drum transcription using partially fixed non-negative matrix factorization

In this paper, a drum transcription algorithm using partially fixed non-negative matrix factorization is presented. The proposed method allows users to identify percussive events in complex mixtures with a minimal training set. The algorithm decomposes the music signal into two parts: percussive part with pre-defined drum templates and harmonic part with undefined entries. The harmonic part is able to adapt to the music content, allowing the algorithm to work in polyphonic mixtures. Drum event times can be simply picked from the percussive activation matrix with onset detection. The system is efficient and robust even with a minimal training set. The recognition rates for the ENST dataset vary from 56.7 to 78.9% for three percussive instruments extracted from polyphonic music.

An Unsupervised Approach to Anomaly Detection in Music Datasets

This paper presents an unsupervised method for systematically identifying anomalies in music datasets. The model integrates categorical regression and robust estimation techniques to infer anomalous scores in music clips. When applied to a music genre recognition dataset, the new method is able to detect corrupted, distorted, or mislabeled audio samples based on commonly used features in music information retrieval. The evaluation results show that the algorithm outperforms other anomaly detection methods and is capable of finding problematic samples identified by human experts. The proposed method introduces a preliminary framework for anomaly detection in music data that can serve as a useful tool to improve data integrity in the future.

On the perceptual relevance of objective source separation measures for singing voice separation

Singing Voice Separation (SVS) is a task which uses audio source separation methods to isolate the vocal component from the background accompaniment for a song mix. This paper discusses the methods of evaluating SVS algorithms, and determines how the current state of the art measures correlate to human perception. A modified ITU-R BS.1543 MUSHRA test is used to get the human perceptual ratings for the outputs of various SVS algorithms, which are correlated with widely used objective measures for source separation quality. The results show that while the objective measures provide a moderate correlation with perceived intelligibility and isolation, they may not adequately assess the overall perceptual quality.

The Relation Between Music Technology and Music Industry

The music industry has changed drastically over the last century and most of its changes and transformations have been technology-driven. Music technology – encompassing musical instruments, sound generators, studio equipment and software, perceptual audio coding algorithms, and reproduction software and devices – has shaped the way music is produced, performed, distributed, and consumed. The evolution of music technology enabled studios and hobbyist producers to produce music at a technical quality unthinkable decades ago and have affordable access to new effects as well as production techniques. Artists explore nontraditional ways of sound generation and sound modification to create previously unheard effects, soundscapes, or even to conceive new musical styles. The consumer has immediate access to a vast diversity of songs and styles and is able to listen to individualized playlists virtually everywhere and at any time. The most disruptive technological innovations during the past 130 years have probably been: 1. The possibility to record and distribute recordings on a large scale through the gramophone. 2. The introduction of vinyl disks enabling high-quality sound reproduction.

Strategies for orca call retrieval to support collaborative annotation of a large archive

The Orchive is a large audio archive of hydrophone recordings of Killer whale (Orcinus orca) vocalizations. Researchers and users from around the world can interact with the archive using a collaborative web-based annotation, visualization and retrieval interface. In addition a mobile client has been written in order to crowdsource Orca call annotation. In this paper we describe and compare different strategies for the retrieval of discrete Orca calls. In addition, the results of the automatic analysis are integrated in the user interface facilitating annotation as well as leveraging the existing annotations for supervised learning. The best strategy achieves a mean average precision of 0.77 with the first retrieved item being relevant 95% of the time in a dataset of 185 calls belonging to 4 types.

Digitale Audiotechnik: Grundlagen

Seit Ende der 1970er Jahre findet im Audiobereich ein grundlegender Systemwandel mit der Ablosung analoger Systeme durch digitale Technologien statt. Wesentliche Grunde fur diesen Wandel sind