The number of publications on acoustic scene classification (ASC) in environmental audio recordings has constantly increased over the last few years. This was mainly stimulated by the annual Detection and Classification of Acoustic Scenes and Events (DCASE) competition with its first edition in 2013. All competitions so far involved one or multiple ASC tasks. With a focus on deep learning based ASC algorithms, this article summarizes and groups existing approaches for data preparation, i.e., feature representations, feature pre-processing, and data augmentation, and for data modeling, i.e., neural network architectures and learning paradigms. Finally, the paper discusses current algorithmic limitations and open challenges in order to preview possible future developments towards the real-life application of ASC systems.

We present a novel approach to the analysis of jazz solos based on the categorisation and annotation of musical units on a middle level between single notes and larger form parts. A guideline during development was the hypothesis that these midlevel units (MLU) correspond to the improvising musicians’ playing ideas and action plans. A system of categories was devised, comprising nine main categories (line, lick, theme, quote, melody, rhythm, expressive, fragment, void), 19 subcategories, and 41 sub-subcategories as well as syntactical rules to encode motivic relationships between units. A set of 140 monophonic jazz solos from various jazz styles (traditional, swing, bebop, hardbop, cool jazz, postbop, free jazz) was annotated manually, resulting in 4939 units in total. The median number of midlevel units is 32 per solo and 13.75 per chorus. The average duration is 2.25 s (SD = 1.57 s), in good agreement with the duration of the subjective present. Overall, the most common main category is lick (45.7% of all units), followed by line (31.5%), but distributions of the main MLU types differ significantly between styles and performers. About one quarter (M = 25.1%, SD = 15.3%) of the annotated units have motivic relations to preceding units. The mean length of consecutive motivic chains is 2.8 (SD = 1.4). The amount of motivic relations varies considerably between performers, but not between styles. Based on these first results, we discuss implications for jazz research and options for further applications of the proposed method

In this paper, we present a feature-based approach for the classification of different playing techniques in bass guitar recordings. The applied audio features are chosen to capture typical instrument sounds induced by 10 different playing techniques. A novel database that consists of approx. 4300 isolated bass notes was assembled for the purpose of evaluation. The usage of domain-specific features in a combination of feature selection and feature space transformation techniques improved the classification accuracy by over 27% points in comparison to a state-of-the-art baseline system. Classification accuracy reached 93.25% and 95.61% for the recognition of plucking and expression styles respectively

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