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7 results for “music detection”
Landmine Detection Using Electromagnetic Time Reversal-Based Methods. Part 1: Classical TR, Iterative TR, DORT and TR-MUSIC
<p>In this repository, you can find the simulation files used to reproduce the results presented in the paper titled "Landmine Detection Using Electromagnetic Time Reversal-Based Methods." If you find these files useful, please cite the paper as it helps acknowledge the work and support future research.</p> <p>This repository is organized into three main folders, each serving a distinct purpose and containing specific types of files:</p> <p><strong>CST-MWS Files</strong>: This folder contains the necessary files to simulate the examples discussed in the paper using CST Microwave Studio (CST-MWS). These files are essential for setting up and running the simulations that replicate the experimental results. </p> <p><strong>TR-MUSIC</strong>: In this folder, you will find the MATLAB m-file required to implement the TR-MUSIC (Time Reversal-MUltiple SIgnal Classification) method proposed in the paper. This MATLAB script is designed to help users apply the TR-MUSIC algorithm to their data, enabling them to detect landmines effectively. </p> <p><strong>2D-GFB</strong>: The 2D-GFB folder contains the MATLAB m-file necessary to run the 2D TR-MUSIC and DORT (Decomposition of the Time Reversal Operator) methods. These advanced techniques are crucial for analyzing two-dimensional data and enhancing the accuracy of landmine detection. </p> <p><br>By organizing the repository in this manner, we aim to make it easier for researchers and practitioners to access and utilize the simulation files effectively. Whether you are replicating the study's results or applying these methods to new datasets, the files and instructions provided should serve as a valuable resource.</p> <p>If you encounter any issues or have questions regarding the use of these files, please do not hesitate to reach out for support. We hope this repository aids your research and contributes to advancements in landmine detection technologies.</p> <p> </p>
Jingju a cappella singing voice test dataset for "An efficient deep learning model for musical onset detection"
<p>Jingju a cappella singing voice test dataset used in the paper "An efficient deep learning model for musical onset detection".</p> <p>Arxiv paper link: <a href="https://arxiv.org/abs/1806.06773">https://arxiv.org/abs/1806.06773</a></p> <p>Supplementary information and code for the paper: <a href="https://github.com/ronggong/musical-onset-efficient">https://github.com/ronggong/musical-onset-efficient</a></p> <p><strong>Content:</strong></p> <ol> <li>ismir_2018_dataset_for_reviewing.zip: audio, syllable boundary and label annotation</li> <li>jingju dataset train test split filenames.xlsx: train and test split filename list</li> </ol> <p><strong>Citation:</strong></p> <pre>@article{gong2018towards, title={Towards an efficient deep learning model for musical onset detection}, author={Gong, Rong and Serra, Xavier}, journal={arXiv preprint arXiv:1806.06773}, year={2018} } </pre> <p><strong>Contact:</strong></p> <p>Rong Gong: rong.gong<at>upf.edu</p>
Binaural detection thresholds and audio quality of speech and music signals in complex acoustic environments
<p>Every-day acoustical environments are often complex, typically comprising one attended target sound in the presence of interfering sounds (e.g., disturbing conversations) and reverberation. Here we assessed binaural detection thresholds and (supra-threshold) binaural audio quality ratings of four distortions types: spectral ripples, non-linear saturation, intensity and spatial modifications applied to speech, guitar, and noise targets in such complex acoustic environments (CAEs). The target and (up to) two masker sounds were either co-located as if contained in a common audio stream, or were spatially separated as if originating from different sound sources. The amount of reverberation was systematically varied. Masker and reverberation had a significant effect on the distortion-detection thresholds of speech signals. Quality ratings were affected by reverberation, whereas the effect of maskers depended on the distortion. The results suggest that detection thresholds and quality ratings for distorted speech in anechoic conditions are also valid for rooms with mild reverberation, but not for moderate reverberation. Furthermore, for spectral ripples, a significant relationship between the listeners’ individual detection thresholds and quality ratings was found. The current results provide baseline data for detection thresholds and audio quality ratings of different distortions of a target sound in CAEs, supporting the future development of binaural auditory models.</p>
M3 (MADHAV Lab Mistake Detection for Music Teaching Database)
<p><strong>Problem Statement</strong>:</p> <p>Music pedagogy, particularly in India, is an oral-aural tradition where the teacher sings along with the learner, mostly one-on-one. In the most common setup, teaching proceeds in a call-and-response fashion, where the teacher sings a short piece, and the learner repeats the same. For instance, the teacher may trim the original piece to include only the segment sung incorrectly by the learner. If the mistake persists or corresponds to an essential concept, the teacher may take a detour to a series of lessons aiming at cultivating that particular concept. This work is a step towards emulating this paradigm computationally. The teacher’s lessons are recorded priori. A learner is presented with these lessons, one piece at a time, sequentially. As the learner repeats the piece, her mistakes are to be detected computationally. Detection of singing mistakes is the primary task of this work. This is to be accomplished by comparing the teacher’s audio with that of the learner. The concept of tonic or keynote is very essential in Indian classical music. Generally, this note, often along with some other essential ones, is played throughout the performance via a drone instrument known as tanpur ¯ a¯. The pedagogy involves practicing with the drone instrument playing in the background.</p> <p> </p> <p><strong>Dataset</strong>:</p> <p>The dataset thus prepared consists of audio files of two teachers and their disjoint set of learners. A file refers to an audio recording for a single lesson and may typically range between a few seconds to a minute in length. The mistakes of the learners are annotated by two expert teachers. They listen to the teacher’s and the learner’s audio and mark the mistakes in terms of the start time, end time, and mistake category. The mistake can fall into one of the following categories:</p> <ul> <li>F: frequency error</li> <li>A: amplitude error</li> <li>P: pronunciation error</li> <li>T: timing error</li> <li>O: other error. This is followed by a short description of the error</li> </ul> <p>For details on dataset curation and preparation or using it for research, please refer to the supplementing paper <a href="https://www.techrxiv.org/articles/preprint/Automatic_Detection_and_Analysis_of_Singing_Mistakes_for_Music_Pedagogy/23269502/1#:~:text=for%20music%20education.-,Novel%20methods%20are%20proposed%20for%20detecting%20singing%20mistakes%20by%20comparing,based%20model%20for%20comparing%20spectrograms.">here</a>.</p>
A Large TV Dataset for Speech and Music Activity Detection
<p>Automatic speech and music activity detection (SMAD) is an enabling task that can help segment, index, and pre-process audio content in radio broadcast and TV programs. However, due to copyright concerns and the cost of manual annotation, the limited availability of diverse and sizeable datasets hinders the progress of state-of-the-art (SOTA) data-driven approaches. We address this challenge by presenting a large-scale dataset containing Mel spectrogram, VGGish, and MFCCs features extracted from around 1600 hours of professionally produced audio tracks and their corresponding noisy labels indicating the approximate location of speech and music segments. The labels are derived from several sources such as subtitles. A test set curated by human annotators is also included as a subset for evaluation. To the best of our knowledge, this dataset is the first large-scale, open-sourced dataset that contains features extracted from professionally produced audio tracks and their corresponding frame-level speech and music annotations. </p>
Dataset for Evaluating Sustain-Pedal Detection from Polyphonic Piano Music
<p>To evaluate methods of sustain-pedal detection from polyphonic piano music, we built a dataset consisting of ten well-known passages of Chopin's music. Ground-truth annotations of this dataset represent sustain-pedal on/off states at every 0.1 second. This annotation was based on the sustain-pedal movement tracked by a dedicated measurement system.</p> <p>Music scores of the ten passages were saved in PDF files. They were performed by a pianist using a Yamaha baby grand piano situated in the studios at Queen Mary University of London. The audio were recorded at 44.1 kHz and 24 bits using the spaced-pair stereo microphone technique. A pair of Earthworks QTC40 omnidirectional condenser microphones was positioned about 50 cm above the strings. </p> <p>We have developed a transfer learning method such that the on/off state of the sustain pedal can be detected at every 0.1 second. The ground-truth annotation and our detection results were saved in <em>transfer-learning-y_segment.npz</em>, which can be loaded using <em>numpy.load </em>in Python. The key for passage name, ground-truth annotation and our detection results is <em>filename_record</em>, <em>y_true</em> and <em>y_pred</em>, respectively.</p>
MINERVA: Benchmarking the detection of musical instruments in unrestricted, non-photorealistic images from the artistic domain
<p>These folders contains all the data and trained models (including a detailed README), needed to replicate the results from the following publication:</p> <blockquote> <p>Matthia Sabatelli, Nikolay Banar, Marie Cocriamont, Eva Coudyzer, Karine Lasaracina, Walter Daelemans, Pierre Geurts & Mike Kestemont, "Advances in Digital Music Iconography. Benchmarking the detection of musical instruments in unrestricted, non-photorealistic images from the artistic domain". Digital Humanities Quarterly (2020).</p> </blockquote> <p>In this paper, we present MINERVA, the first benchmark dataset for the detection of musical instruments in non-photorealistic, unrestricted image collections from the realm of the visual arts. This effort is situated against the scholarly background of music iconography, an interdisciplinary field at the intersection of musicology and art history. We benchmark a number of state-of-the-art systems for image classification and object detection. Our results demonstrate the feasibility of the task but also highlight the significant challenges which this artistic material poses to computer vision. All the corresponding code, necessary for extending or replicating our work, is freely available for reuse (CC-BY) from an <a href="https://github.com/paintception/MINeRVA">open code repository</a>.</p> <p>This work has been generously funded by the Belgian Federal Research Agency BELSPO under the BRAIN-be program (project title: 'INSIGHT: Intelligent Neural Systems as Integrated Heritage Tools').</p> <p>Project website: <a href="https://hosting.uantwerpen.be/insight/">https://hosting.uantwerpen.be/insight/</a></p>
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OpenNeuro
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