Convolutional neural networks for the classification of guitar effects and extraction of the parameter settings of single and multi-guitar effects from instrument mixes

Guitar effects are commonly used in popular music to shape the guitar sound to fit specific genres, or to create more variety within musical compositions. The sound not only is determined by the choice of the guitar effect, but also heavily depends on the parameter settings of the effect. Previous research focused on the classification of guitar effects and extraction of their parameter settings from solo guitar audio recordings. However, more realistic is the classification and extraction from instrument mixes. This work investigates the use of convolution neural networks (CNNs) for the classification and parameter extraction of guitar effects from audio samples containing guitar, bass, keyboard, and drums. The CNN was compared to baseline methods previously proposed, like support vector machines and shallow neural networks together with predesigned features. On two datasets, the CNN achieved classification accuracies 1-5% above the baseline accuracy, achieving up to 97.4% accuracy. With parameter values between 0.0 and 1.0, mean absolute parameter extraction errors of below 0.016 for the distortion, below 0.052 for the tremolo, and below 0.038 for the slapback delay effect were achieved, matching or surpassing the presumed human expert error of 0.05. The CNN approach was found to generalize to further effects, achieving mean absolute parameter extraction errors below 0.05 for the chorus, phaser, reverb, and overdrive effect. For sequentially applied combinations of distortion, tremolo, and slapback delay, the mean extraction error slightly increased from the performance for the single effects to the range of 0.05 to 0.1. The CNN was found to be moderately robust to noise and pitch changes of the background instrumentation suggesting that the CNN extracted meaningful features

Links und rechts verbinden : Räumliches Hören mit Cochlea-Implantaten

Viele Menschen, die ein Hörgerät nutzen, haben oft Schwierigkeiten, bei Hinter-grundlärm ihren Gesprächs-partner zu verstehen. Insbesondere bei sogenannten Cochlea-Implantaten (CI) liegt dies wesentlich daran, dass nicht alle Funktionen eines Ohres übernommen werden können. Um CI-Trägern räumliches Hören sowie verbessertes Sprachverstehen zu ermöglichen, arbeiten das Institut für Informationsverarbeitung (TNT) der Leibniz Universität Hannover sowie das Deutsche Hörzentrum (DHZ) im Bereich Hörforschung gemeinsam an technischen Lösungen

Analysis of the impact of data compression on condition monitoring algorithms for ball screws

The overall equipment effectiveness (OEE) is a management ratio to evaluate the added value of machine tools. Unplanned machine downtime reduces the operational availability and therefore, the OEE. Increased machine costs are the consequence. An important cause of unplanned machine downtimes is the total failure of ball screws of the feed axes due to wear. Therefore, monitoring of the condition of ball screws is important. Common concepts rely on high-frequency acceleration sensors from external control systems to detect a change of the condition. For trend and detailed damage analysis, large amounts of data are generated and stored over a long time period (>5 years), resulting in corresponding data storage costs. Additional axes or machine tools increase the data volume further, adding to the total storage costs. To minimize these costs, data compression or source coding has to be applied. To achieve maximum compression ratios, lossy coding algorithms have to be used, which introduce distortion in a signal. In this work, the influence of lossy coding algorithms on a condition monitoring algorithm (CMA) using acceleration signals is investigated. The CMA is based on principal component analysis and uses 17 features such as standard deviation to predict the preload condition of a ball screw. It is shown that bit rate reduction through lossy compression algorithms is possible without affecting the condition monitoring - as long as the compression algorithm is known. In contrast, an unknown compression algorithm reduces the classification accuracy of condition monitoring by about 20 % when coding with a quantizer resolution of 4 bit/sample

Binaurales Hören : Auf dem Weg zum räumlichen Hören mit Cochlea-Implantaten

Räumliches Hören soll verbessert werden. Während die Auditory Prosthetic Group (APG) der HNO-Klinik der Medizinische Hochschule Hannover (MHH) sich auf binaurale Soundcodierungsstrategien für Cochlea-Implantate sowie grundlegende Experimente am Menschen konzentriert, werden Wissenschaftler vom Institut für Informationsverarbeitung (TNT) der Leibniz Universität Hannover (LUH) an Kompressionsalgorithmen zur drahtlosen Übertragung von Audio und elektrischen Signalen für bilaterale Cochlea-Implantate arbeiten