Final Research Report for Sound Design and Audio Player

This deliverable describes the work on Task 4.3 Algorithms for sound design and feature developments for audio player. The audio player runs on the in-store player (ISP) and takes care of rendering the music playlists via beat-synchronous automatic DJ mixing, taking advantage of the rich musical content description extracted in T4.2 (beat markers, structural segmentation into intro and outro, musical and sound content classification). The deliverable covers prototypes and final results on: (1) automatic beat-synchronous mixing by beat alignment and time stretching – we developed an algorithm for beat alignment and scheduling of time-stretched tracks; (2) compensation of play duration changes introduced by time stretching – in order to make the playlist generator independent of beat mixing, we chose to readjust the tempo of played tracks such that their stretched duration is the same as their original duration; (3) prospective research on the extraction of data from DJ mixes – to alleviate the lack of extensive ground truth databases of DJ mixing practices, we propose steps towards extracting this data from existing mixes by alignment and unmixing of the tracks in a mix. We also show how these methods can be evaluated even without labelled test data, and propose an open dataset for further research; (4) a description of the software player module, a GUI-less application to run on the ISP that performs streaming of tracks from disk and beat-synchronous mixing. The estimation of cue points where tracks should cross-fade is now described in D4.7 Final Research Report on Auto-Tagging of Music.EC/H2020/688122/EU/Artist-to-Business-to-Business-to-Consumer Audio Branding System/ABC D

Automatic music transcription: challenges and future directions

Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects

Vocal Source Separation for Carnatic Music

Carnatic Music is a Classical music form that originates from the South of India and is extremely varied from Western genres. Music Information Retrieval (MIR) has predominantly been used to tackle problems in western musical genres and cannot be adapted to non western musical styles like Carnatic Music due to the fundamental difference in melody, rhythm, instrumentation, nature of compositions and improvisations. Due to these conceptual differences emerged MIR tasks specific for the use case of Carnatic Music. Researchers have constantly been using domain knowledge and technology driven ideas to tackle tasks like Melodic analysis, Rhythmic analysis and Structural segmentation. Melodic analysis of Carnatic Music has been a cornerstone in MIR research and heavily relies on the singing voice because the singer offers the main melody. The problem is that the singing voice is not isolated and has melodic, percussion and drone instruments as accompaniment. Separating the singing voice from the accompanying instruments usually comes with issues like bleeding of the accompanying instruments and loss of melodic information. This in turn has an adverse effect on the melodic analysis. The datasets used for Carnatic-MIR are concert recordings of different artistes with accompanying instruments and there is a lack of clean isolated singing voice tracks. Existing Source Separation models are trained extensively on multi-track audio of the rock and pop genre and do not generalize well for the use case of Carnatic music. How do we improve Singing Voice Source Separation for Carnatic Music given the above constraints? In this work, the possible contributions to mitigate the existing issue are ; 1) Creating a dataset of isolated Carnatic music stems. 2) Reusing multi-track audio with bleeding from the Saraga dataset. 3) Retraining and fine tuning existing State of the art Source Separation models. We hope that this effort to improve Source Separation for Carnatic Music can help overcome existing shortcomings and generalize well for Carnatic music datasets in the literature and in turn improve melodic analysis of this music culture

Occurrence and Distribution of Cyanobacteria and their Toxins in Silver Lake, New Hampshire

A study of Silver Lake, NH was performed as part of a 5-lake assessment of cyanobacteria prevalence and distribution. Multi-parameter fluorescence probe measurements of chlorophyll a and cyanobacteria concentrations (PC, phycocyanin fluorescence) were evaluated in addition to physical and chemical characteristics of the lake. Silver Lake did not exhibit summer stratification suggesting a recent mixing event. It had oligotrophic levels of Chlorophyll a (1.93 ± 0.06 mg L-1) and of TP (10.10 mg L-1), yet PC levels were the highest of all the lakes studied (248691 ± 963 Microcystis cell equivalents mL-1). The cyanobacteria Microcystis dominated the phytoplankton community. Microcystin levels varied from a mean 72.43 ± 21.21 pg mL-1 in transect water to 137.69 ± 46.9 pg mL-1 in sediment water. Chlorophyll distribution was rather homogeneous while cyanobacteria levels were highest towards the shallow, embayed NE part of the lake where a section of a State Park beach is located. Implications include potential increase in exposure to toxins by water users. Heterogeneous distribution of cyanobacteria emphasizes the importance of extensive sampling beyond pelagic sampling sites to more accurately inform decision-making regarding health and safety of water bodies

Data-Driven Sound Track Generation

Background music is often used to generate a specific atmosphere or to draw our attention to specific events. For example in movies or computer games it is often the accompanying music that conveys the emotional state of a scene and plays an important role for immersing the viewer or player into the virtual environment. In view of home-made videos, slide shows, and other consumer-generated visual media streams, there is a need for computer-assisted tools that allow users to generate aesthetically appealing music tracks in an easy and intuitive way. In this contribution, we consider a data-driven scenario where the musical raw material is given in form of a database containing a variety of audio recordings. Then, for a given visual media stream, the task consists in identifying, manipulating, overlaying, concatenating, and blending suitable music clips to generate a music stream that satisfies certain constraints imposed by the visual data stream and by user specifications. It is our main goal to give an overview of various content-based music processing and retrieval techniques that become important in data-driven sound track generation. In particular, we sketch a general pipeline that highlights how the various techniques act together and come into play when generating musically plausible transitions between subsequent music clips

AUTOMATIC SUBGROUPING OF MULTITRACK AUDIO

Subgrouping is a mixing technique where the outputs of a subset of audio tracks in a multitrack are summed to a single audio bus. This is done so that the mix engineer can apply signal processing to an entire subgroup, speed up the mix work flow and manipu-late a number of audio tracks at once. In this work, we investigate which audio features from a set of 159 can be used to automati-cally subgroup multitrack audio. We determine a subset of audio features from the original 159 audio features to use for automatic subgrouping, by performing feature selection using a Random For-est classifier on a dataset of 54 individual multitracks. We show that by using agglomerative clustering on 5 test multitracks, the entire set of audio features incorrectly clusters 35.08 % of the audio tracks, while the subset of audio features incorrectly clusters only 7.89 % of the audio tracks. Furthermore, we also show that using the entire set of audio features, ten incorrect subgroups are created. However, when using the subset of audio features, only five incor-rect subgroups are created. This indicates that our reduced set of audio features provides a significant increase in classification ac-curacy for the creation of subgroups automatically. 1

Database of audio records

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