Motifs, Phrases, and Beyond: The Modelling of Structure in Symbolic Music Generation

Modelling musical structure is vital yet challenging for artificial intelligence systems that generate symbolic music compositions. This literature review dissects the evolution of techniques for incorporating coherent structure, from symbolic approaches to foundational and transformative deep learning methods that harness the power of computation and data across a wide variety of training paradigms. In the later stages, we review an emerging technique which we refer to as “sub-task decomposition" that involves decomposing music generation into separate high-level structural planning and content creation stages. Such systems incorporate some form of musical knowledge or neuro-symbolic methods by extracting melodic skeletons or structural templates to guide the generation. Progress is evident in capturing motifs and repetitions across all three eras reviewed, yet modelling the nuanced development of themes across extended compositions in the style of human composers remains difficult. We outline several key future directions to realize the synergistic benefits of combining approaches from all eras examined

Open source MATLAB implementation of MD_DSQ coder

This software provides an open source MATLAB implementation of the Multiple Description Delta Sigma Quantization Coder published in the paper: "Low Delay Robust Audio Coding by Noise Shaping, Fractional Sampling, and Source Prediction", Jan Østergaard. IEEE Data Compression Conference

Contextual inquiries observations - clinicians assessments of aided performance in three public hearing clinics in Denmark

The data has been collected as a part of a Danish national study on better hearing rehabilitation (BEAR). In this study focus is on how hearing care professionals assess outcome of hearing aid rehabilitation. Data is retrieved from a larger dataset analyzed in NVivo (Nvivo qualitative data analysis Software; QSR International Pty Ltd. Version 12, 2018). The empirical data is collected during contextual inquiries observations and interviews with 17 professionals in three Danish public hearing clinics

EEG data of continuous listening of music and speech

This dataset contains EEG recordings from 18 subjects listening to continuous sound, either speech or music. Continuous audio stimuli were presented to listeners in trials of 70 seconds from one loudspeaker located 150 cm in from of them. They were instructed to attentively listen to the sound during the whole trial. All listeners were native Danish speakers and were presented with 5 different types of audio stimuli: Instrumental music: Excerpt of Disney songs: Excerpts of polyphonic Disney songs with no lyrics. The melody line from the original version was replaced by a similar melody played by a synthetic cello (Referred to as MC: Music Cello in the dataset). Music with understood lyrics: Excerpts of polyphonic Disney songs with lyrics in Danish, understood by the listeners (Referred to as MD: Music Danish in the dataset). Music with non-understood lyrics: Excerpts of polyphonic Disney songs with lyrics in Finnish, not understood by the listeners (Referred to as MF: Music Finnish in the dataset). Understood speech: Excerpts of an audiobook in Danish read by a woman, understood by the listeners (Referred to as SD: Speech Danish in the dataset). Non-Understood speech: Excerpts of an audiobook in Finnish read by a woman, understood by the listeners (Referred to as SF: Speech Danish in the dataset). Data were recorded using a 64-channels g.HIamp-Research system and digitalized at a sampling rate of 2400 Hz. The dataset contains pre-processed EEG data (see pre-processing step applies to the data below), for each listener. Trials with large noise artefacts have been removed. The processed folder contains data used in Simon, A. et al. (2022) Cortical linear encoding and decoding of sounds: Differences between naturalistic speech and music listening. (Submitted). The processed data contains EEG data and an aligned audio envelope for each category of audio stimuli. The MATLAB script contains the processing applied to obtain it. The dataset was created within the InHear project. For more information, [email protected] Preprocessing done -re-reference to average channels -downsampling to 512Hz -bandpass filter 0.5-45 Hz -ICA decomposition using SOBI algorithm -removed eyes and noise component

EEG data of continuous listening of music and speech

This dataset contains EEG recordings from 18 subjects listening to continuous sound, either speech or music. Continuous audio stimuli were presented to listeners in trials of 70 seconds from one loudspeaker located 150 cm in from of them. They were instructed to attentively listen to the sound during the whole trial. All listeners were native Danish speakers and were presented with 5 different types of audio stimuli: Instrumental music: Excerpt of Disney songs: Excerpts of polyphonic Disney songs with no lyrics. The melody line from the original version was replaced by a similar melody played by a synthetic cello (Referred to as MC: Music Cello in the dataset). Music with understood lyrics: Excerpts of polyphonic Disney songs with lyrics in Danish, understood by the listeners (Referred to as MD: Music Danish in the dataset). Music with non-understood lyrics: Excerpts of polyphonic Disney songs with lyrics in Finnish, not understood by the listeners (Referred to as MF: Music Finnish in the dataset). Understood speech: Excerpts of an audiobook in Danish read by a woman, understood by the listeners (Referred to as SD: Speech Danish in the dataset). Non-Understood speech: Excerpts of an audiobook in Finnish read by a woman, understood by the listeners (Referred to as SF: Speech Danish in the dataset). Data were recorded using a 64-channels g.HIamp-Research system and digitalized at a sampling rate of 2400 Hz. The dataset contains pre-processed EEG data (see pre-processing step applies to the data below), for each listener. Trials with large noise artefacts have been removed. The processed folder contains data used in Simon, A. et al. (2022) Cortical linear encoding and decoding of sounds: Differences between naturalistic speech and music listening. (Submitted). The processed data contains EEG data and an aligned audio envelope for each category of audio stimuli. The MATLAB script contains the processing applied to obtain it. The dataset was created within the InHear project. For more information, [email protected] Preprocessing done -re-reference to average channels -downsampling to 512Hz -bandpass filter 0.5-45 Hz -ICA decomposition using SOBI algorithm -removed eyes and noise component

Generalized Approximate Message Passing Practical 2D Phase Transition Simulations Dataset 2

This deposition contains the results from a simulation of phase transitions for various practical 2D and 3D problem suites when using the Generalised Approximate Message Passing (GAMP) reconstruction algorithm. The deposition consists of: Five HDF5 databases containing the results from the phase transition simulations (gamp_practical_2d_phase_transitions_ID_[0-4]_of_5.hdf5). The Python script which was used to create the databases (gamp_practical_2d_phase_transitions.py). A Python module with tools needed to run the simulations (gamp_pt_tools.py). MD5 and SHA256 checksums of the databases and Python scripts (gamp_practical_2d_phase_transitions.MD5SUMS / gamp_practical_2d_phase_transitions.SHA256SUMS). The HDF5 databases are licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python scripts are licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) . The files are provided as-is with no warranty as detailed in the above mentioned licenses

Algorithms for Reconstruction of Undersampled Atomic Force Microscopy Images Dataset

This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using a variety of interpolation and reconstruction methods. The deposition consists of: An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (reconstruction_goblet_ID_0_of_1.hdf5). The Python script which was used to create the database (reconstruction_goblet.py). Auxillary Python scripts needed to run the simulations (optim_reconstructions.py, it_reconstruction.py, interp_reconstructions.py, gamp_reconstructions.py, and utils.py). MD5 and SHA256 checksums of the database and Python script files (reconstruction_goblet.MD5SUMS, reconstruction_goblet.SHA256SUMS). The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) . The files are provided as-is with no warranty as detailed in the above mentioned licenses. The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" and "Atomic Force Microscopy Images of Various Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573 and http://dx.doi.org/10.5281/zenodo.60434. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset

IoT device identification dataset

This is the main record in the IoT device measurements connected to the paper "Identification of IoT Devices using Experimental Radio Spectrum Dataset and Deep Learning". This is a connecting dataset that references all the actual data. The records are split because of the large size of the dataset. The records containing the data are found at the following DOIs: Raw data Other room: 10.5281/zenodo.3646427 Upstairs: 10.5281/zenodo.3641580 Same room: 10.5281/zenodo.3638163 Background measurement: 10.5281/zenodo.3638139 Multi user data (No fading): 10.5281/zenodo.3754210 Multi user data: 10.5281/zenodo.3753003 Cut and dimensionality reduced measurements: 10.5281/zenodo.3752981 Common parameters for all the measurements: Frequency: 863-870 MHz (center 866,5 MHz) Sample Frequency: 10 MSPS Date of measurement: 15 November 2018 Location: Connectivity Lab, Fredrik Bajers Vej 7C, Aalborg University, Denmar