3 research outputs found
Musical Cross Synthesis using Matrix Factorisation
The focus of this work is to explore a new method for the creative analysis and manipulation of musical audio content. Given a target song and a source song, the goal is reconstruct the harmonic and rhythmic structure of the target with the timbral components from the source, in such a way that so that both the target and the source material are recognizable by the listener. We refer to this operation as musical cross-synthesis. For this purpose, we propose the use of a Matrix Factorisation method, more specifically, Shift-Invariant Probabilistic Latent Component Analysis (PLCA). The input to the PLCA algorithm are beat synchronous CQT basis functions of the source whose temporal activations are used to approximate the CQT of the target. Using the shift invariant property of the PLCA allows each basis function to be subjected to a range of possible pitch shifts which increases the flexibility of the source to represent the target. To create the resulting musical cross-synthesis the beat synchronous, pitch-shifted CQT basis functions are inverted and concatenated in time
Play as You Like: Timbre-enhanced Multi-modal Music Style Transfer
Style transfer of polyphonic music recordings is a challenging task when
considering the modeling of diverse, imaginative, and reasonable music pieces
in the style different from their original one. To achieve this, learning
stable multi-modal representations for both domain-variant (i.e., style) and
domain-invariant (i.e., content) information of music in an unsupervised manner
is critical. In this paper, we propose an unsupervised music style transfer
method without the need for parallel data. Besides, to characterize the
multi-modal distribution of music pieces, we employ the Multi-modal
Unsupervised Image-to-Image Translation (MUNIT) framework in the proposed
system. This allows one to generate diverse outputs from the learned latent
distributions representing contents and styles. Moreover, to better capture the
granularity of sound, such as the perceptual dimensions of timbre and the
nuance in instrument-specific performance, cognitively plausible features
including mel-frequency cepstral coefficients (MFCC), spectral difference, and
spectral envelope, are combined with the widely-used mel-spectrogram into a
timber-enhanced multi-channel input representation. The Relativistic average
Generative Adversarial Networks (RaGAN) is also utilized to achieve fast
convergence and high stability. We conduct experiments on bilateral style
transfer tasks among three different genres, namely piano solo, guitar solo,
and string quartet. Results demonstrate the advantages of the proposed method
in music style transfer with improved sound quality and in allowing users to
manipulate the output
Abstracts on Radio Direction Finding (1899 - 1995)
The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography).
Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM.
The contents of these files are:
1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format];
2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format];
3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion