Loopers become more and more popular due to their growing features and capabilities, not only in live performances but also as a rehearsal tool. These effect units record a phrase and play it back in a loop. The start and stop positions of the recording are typically the player’s start and stop taps on a foot switch. However, if these cues are not entered precisely in time, an annoying, audible gap may occur between the repetitions of the phrase. We propose an algorithm that analyzes the recorded phrase and aligns start and stop positions in order to remove audible gaps. Efficiency, accuracy and robustness are achieved by including the phase information of the onset detection function’s STFT within the beat estimation process. Moreover, the proposed algorithm satisfies the response time required for the live application of beat alignment. We show that robustness is achieved for phrases of sparse rhythmic content for which there is still sufficient information to derive underlying beats.

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Usually, a sound designer achieves artistic goals by editing and processing the pre-recorded sound samples. To assist navigation in the vast amount of sounds, the sound metadata is used: it provides small free-form textual descriptions of the sound file content. One can search through the keywords or phrases in the metadata to find a group of sounds that can be suitable for a task. Unfortunately, the relativity of the sound design terms complicate the search, making the search process tedious, prone to errors and by no means supportive of the creative flow. Another way to approach the sound search problem is to use sound analysis. In this paper we present a simple method for analyzing the temporal evolution of the “whoosh” sound, based on the per-band piecewise linear function approximation of the sound envelope signal. The method uses spectral centroid and fuzzy membership functions to estimate a degree to which the sound energy moves upwards or downwards in the frequency domain along the audio file. We evaluated the method on a generated dataset, consisting of white noise recordings processed with different variations of modulated bandpass filters. The method was able to correctly identify the centroid movement directions in 77% sounds from a synthetic dataset.

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The same music composition can be performed in different ways, and the differences in performance aspects can strongly change the expression and character of the music. Experienced musicians tend to have their own performance style, which reflects their personality, attitudes and beliefs. In this paper, we present a datadriven analysis of the performance style of two master violinists, Jascha Heifetz and David Fyodorovich Oistrakh to find out their differences. Specifically, from 26 gramophone recordings of each of these two violinists, we compute features characterizing performance aspects including articulation, energy, and vibrato, and then compare their style in terms of the accents and legato groups of the music. Based on our findings, we propose algorithms to synthesize violin audio solo recordings of these two masters from scores, for music compositions that we either have or have not observed in the analysis stage. To our best knowledge, this study represents the first attempt that computationally analyzes and synthesizes the playing style of master violinists.

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In musical timbre, two of the most commonly used perceptual dimensions are warmth and brightness. In this study, we develop a model capable of accurately controlling the warmth and brightness of an audio source using a single parameter. To do this, we first identify the most salient audio features associated with the chosen descriptors by applying dimensionality reduction to a dataset of annotated timbral transformations. Here, strong positive correlations are found between the centroid of various spectral representations and the most salient principal components. From this, we build a system designed to manipulate the audio features directly using a combination of linear and nonlinear processing modules. To validate the model, we conduct a series of subjective listening tests, and show that up to 80% of participants are able to allocate the correct term, or synonyms thereof, to a set of processed audio samples. Objectively, we show low Mahalanobis distances between the processed samples and clusters of the same timbral adjective in the low-dimensional timbre space.

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This paper contains the results of a study making use of a set of B-format soundscape recordings, presented in stereo UHJ format as part of an online listening test, in order to investigate the relationship between soundscape categorisation and subjective evaluation. Test participants were presented with a set of soundscapes and asked to rate them using the Self-Assessment Manikin (SAM) and in terms of three soundscape categories: natural, human, and mechanical. They were also asked to identify the important sound sources present in each soundscape. Results show significant relationships between soundscape categorisation and the SAM, and indicate particularly significant sound sources that can affect these ratings.

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This paper describes the development and application of a machine listening system for the automated testing of implementation equivalence in music signal processing effects which contain a high level of randomized time variation. We describe a mathematical model of generalized randomization in audio effects and explore different representations of the effect’s data. We then propose a set of classifiers to reliably determine if two implementations of the same randomized audio effect are functionally equivalent. After testing these classifiers against each other and against a set of human listeners we find the best implementation and determine that it agrees with the judgment of human listeners with an F1-Score of 0.8696.

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Applause is the result of many individuals rhythmically clapping their hands. Applause recordings exhibit a certain temporal, timbral and spatial structure: claps originating from a distinct direction (i.e, from a particular person) usually have a similar timbre and occur in a quasi-periodic repetition. Traditional upmix approaches for blind mono-to-stereo upmix do not consider these properties and may therefore produce an output with suboptimal perceptual quality to be attributed to a lack of plausibility. In this paper, we propose a blind upmixing approach of applause-like signals which aims at preserving the natural structure of applause signals by incorporating periodicity and timbral similarity of claps into the upmix process and therefore supporting plausibility of the artificially generated spatial scene. The proposed upmix approach is evaluated by means of a subjective preference listening test.

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This paper presents the co-authorship network of the DAFx conference series, from its inception in 1998 to the present, along with subsequent analysis. In total 1,281 unique authors have contributed 1,175 unique submissions to this conference series. The co-authorship network is revealed to contain a large weakly connected component containing 667 authors (≈52% of the total network). The size of this component compares well to previous studies of other conference series of similar age and scope. Within this connected component, 24 communities were detected using the Louvain method. While some communities have formed based on geographic proximity, links between communities are observed. This shows a high level of collaboration in the network, possibly due to the speciality of the conference and the movement of academics throughout Europe.

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