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Skip to Search Results- 17Deep learning
- 3Music information retrieval
- 2Instrument transcription
- 1 Image denoising
- 13D Semantic Segmentation
- 1Artificial intelligence
- 13Graduate and Postdoctoral Studies (GPS), Faculty of
- 13Graduate and Postdoctoral Studies (GPS), Faculty of/Theses and Dissertations
- 2Computing Science, Department of
- 1Computing Science, Department of/Journal Articles (Computing Science)
- 1Computing Science, Department of/Conference Papers (Computing Science)
- 1Concordia University of Edmonton
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Fall 2015
Music transcription is the process of extracting the pitch and timing of notes that occur in an audio recording and writing the results as a music score, commonly referred to as sheet music. Manually transcribing audio recordings is a difficult and time-consuming process, even for experienced...
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2017
Music transcription involves the transformation of an audio recording to common music notation, colloquially referred to as sheet music. Manually transcribing audio recordings is a difficult and time-consuming process, even for experienced musicians. In response, several algorithms have been...
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Nanocrystal-based optical diffusers for white LED lighting: inverse design achieved by machine learning technologies
DownloadSpring 2023
Illumination receives a great deal of attention as white lighting-emitting diodes (WLEDs) become energy-efficient light sources in households and commercial buildings, on streets and highways, and at stadiums and construction sites. In general, lenses and mirrors are used to control the spatial...
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Prognostics and Maintenance Decision-making for Mechanical Systems based on Condition Monitoring Data
DownloadSpring 2024
Condition-based maintenance (CBM) is a maintenance approach that uses condition monitoring data to make maintenance decisions. The goal of CBM is to avoid machine shutdowns, reduce maintenance costs, and improve system safety. In modern mechanical systems, a wide range of sensors are used to...
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Spring 2018
Hosseinzadeh Heydarabad,Sepideh
In this work we address the problem of fast shadow detection from single images of natural scenes. Different from traditional methods that employ expensive optimization methods, we propose a fast semantic-aware Convolutional Neural Network learning framework which trains on different kinds of...
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Training Deep Convolutional Networks with Unlimited Synthesis of Musical Examples for Multiple Instrument Recognition
Download2018
Sethi, R., Weninger, N., Hindle, Abram, Bulitko, V., Frishkopf, M.
Deep learning has yielded promising results in music information retrieval and other domains compared to machine learning algorithms trained on hand-crafted feature representations, but is often limited by the availability of data and vast hyper-parameter space. It is difficult to obtain large...