Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Fazekas, George"'
Autor:
Shatri, Elona, Fazekas, George
Optical Music Recognition (OMR) automates the transcription of musical notation from images into machine-readable formats like MusicXML, MEI, or MIDI, significantly reducing the costs and time of manual transcription. This study explores knowledge di
Externí odkaz:
http://arxiv.org/abs/2408.15002
Efficient audio representations in a compressed continuous latent space are critical for generative audio modeling and Music Information Retrieval (MIR) tasks. However, some existing audio autoencoders have limitations, such as multi-stage training p
Externí odkaz:
http://arxiv.org/abs/2408.06500
Autor:
Weck, Benno, Manco, Ilaria, Benetos, Emmanouil, Quinton, Elio, Fazekas, George, Bogdanov, Dmitry
Multimodal models that jointly process audio and language hold great promise in audio understanding and are increasingly being adopted in the music domain. By allowing users to query via text and obtain information about a given audio input, these mo
Externí odkaz:
http://arxiv.org/abs/2408.01337
Autor:
Vanka, Soumya Sai, Steinmetz, Christian, Rolland, Jean-Baptiste, Reiss, Joshua, Fazekas, George
Mixing style transfer automates the generation of a multitrack mix for a given set of tracks by inferring production attributes from a reference song. However, existing systems for mixing style transfer are limited in that they often operate only on
Externí odkaz:
http://arxiv.org/abs/2407.08889
Music source separation is focused on extracting distinct sonic elements from composite tracks. Historically, many methods have been grounded in supervised learning, necessitating labeled data, which is occasionally constrained in its diversity. More
Externí odkaz:
http://arxiv.org/abs/2311.13058
Although the design and application of audio effects is well understood, the inverse problem of removing these effects is significantly more challenging and far less studied. Recently, deep learning has been applied to audio effect removal; however,
Externí odkaz:
http://arxiv.org/abs/2308.16177
Jazz pianists often uniquely interpret jazz standards. Passages from these interpretations can be viewed as sections of variation. We manually extracted such variations from solo jazz piano performances. The JAZZVAR dataset is a collection of 502 pai
Externí odkaz:
http://arxiv.org/abs/2307.09670
Publikováno v:
Paper number 10653, 154th AES Convention 2023
The integration of artificial intelligence (AI) technology in the music industry is driving a significant change in the way music is being composed, produced and mixed. This study investigates the current state of AI in the mixing workflows and its a
Externí odkaz:
http://arxiv.org/abs/2304.03407
Pitch and meter are two fundamental music features for symbolic music generation tasks, where researchers usually choose different encoding methods depending on specific goals. However, the advantages and drawbacks of different encoding methods have
Externí odkaz:
http://arxiv.org/abs/2301.13383
Autor:
Muradeli, John, Vahidi, Cyrus, Wang, Changhong, Han, Han, Lostanlen, Vincent, Lagrange, Mathieu, Fazekas, George
Joint time-frequency scattering (JTFS) is a convolutional operator in the time-frequency domain which extracts spectrotemporal modulations at various rates and scales. It offers an idealized model of spectrotemporal receptive fields (STRF) in the pri
Externí odkaz:
http://arxiv.org/abs/2204.08269