SymPAC: Scalable Symbolic Music Generation With Prompts And Constraints

Autor: Chen, Haonan, Smith, Jordan B. L., Spijkervet, Janne, Wang, Ju-Chiang, Zou, Pei, Li, Bochen, Kong, Qiuqiang, Du, Xingjian

Progress in the task of symbolic music generation may be lagging behind other tasks like audio and text generation, in part because of the scarcity of symbolic training data. In this paper, we leverage the greater scale of audio music data by applyin

Externí odkaz: http://arxiv.org/abs/2409.03055

Multi-year characterisation of the broad-band emission from the intermittent extreme BL Lac 1ES~2344+514

Autor: Abe, H., Abe, S., Acciari, V. A., Agudo, I., Aniello, T., Ansoldi, S., Antonelli, L. A., Engels, A. Arbet, Arcaro, C., Artero, M., Asano, K., Baack, D., Babić, A., Baquero, A., de Almeida, U. Barres, Batković, I., Baxter, J., González, J. Becerra, Bernardini, E., Bernete, J., Berti, A., Besenrieder, J., Bigongiari, C., Biland, A., Blanch, O., Bonnoli, G., Bošjak, Ž, Burelli, I., Busetto, G., Campoy-Ordaz, A., Carosi, A., Carosi, R., Carretero-Castrillo, M., Castro-Tirado, A. J., Chai, Y., Cifuentes, A., Cikota, S., Colombo, E., Contreras, J. L., Cortina, J., Covino, S., D'Amico, G., D'Ammando, F., D'Elia, V., Da Vela, P., Dazzi, F., De Angelis, A., De Lotto, B., Del Popolo, A., Delfino, M., Delgado, J., Mendez, C. Delgado, Depaoli, D., Di Pierro, F., Di Venere, L., Prester, D. Dominis, Dorner, D., Doro, M., Elsaesser, D., Emery, G., Escudero, J., Fariña, L., Fattorini, A., Foffano, L., Font, L., Fukami, S., Fukazawa, Y., López, R. J. García, Gasparyan, S., Gaug, M., Paiva, J. G. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Grau, R., Green, J. G., Hadasch, D., Hahn, A., Heckmann, L., Herrera, J., Hrupec, D., Hütten, M., Imazawa, R., Inada, T., Iotov, R., Ishio, K., Martínez, I. Jiménez, Jormanainen, J., Kerszberg, D., Kluge, G. W., Kobayashi, Y., Kouch, P. M., Kubo, H., Kushida, J., Lezáun, M. Láinez, Lamastra, A., Leone, F., Lindfors, E., Linhoff, L., Lombardi, S., Longo, F., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Fraga, B. Machado de Oliveira, Majumdar, P., Makariev, M., Maneva, G., Mang, N., Manganaro, M., Mariotti, M., Martínez, M., Martínez-Chicharro, M., Mas-Aguilar, A., Mazin, D., Menchiari, S., Mender, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., González, M. Molero, Molina, E., Mondal, H. A., Moralejo, A., Morcuende, D., Nakamori, T., Nanci, C., Neustroev, V., Nigro, C., Nikolić, L., Nishijima, K., Ekoume, T. Njoh, Noda, K., Nozaki, S., Ohtani, Y., Okumura, A., Otero-Santos, J., Paiano, S., Palatiello, M., Paneque, D., Paoletti, R., Paredes, J. M., Pavlović, D., Persic, M., Pihet, M., Pirola, G., Podobnik, F., Moroni, P. G. Prada, Prandini, E., Principe, G., Priyadarshi, C., Rhode, W., Ribó, M., Rico, J., Righi, C., Sahakyan, N., Saito, T., Satalecka, K., Saturni, F. G., Schleicher, B., Schmidt, K., Schmuckermaier, F., Schubert, J. L., Schweizer, T., Sciaccaluga, A., Sitarek, J., Spolon, A., Stamerra, A., Strišović, J., Strom, D., Suda, Y., Tajima, H., Takeishi, R., Tavecchio, F., Temnikov, P., Terauchi, K., Terzić, T., Teshima, M., Tosti, L., Truzzi, S., Tutone, A., Ubach, S., van Scherpenberg, J., Ventura, S., Verguilov, V., Viale, I., Vigorito, C. F., Vitale, V., Walter, R., Wunderlich, C., Leto, T. Yamamoto M. Perri F. Verrecchia C., Das, S., Chatterjee, R., Raiteri, C. M., Villata, M., Semkov, E., Ibryamov, S., Bachev, R., Strigachev, A., Damljanovic, G., Vince, O., Jovanovic, M. D., Stojanovic, M., Larionov, V. M., Grishina, T. S., Kopatskaya, E. N., Larionova, E. G., Morozova, D. A., Savchenko, S. S., Troitskiy, I. S., Troitskaya, Y. V., Vasilyev, A. A., Chen, W. P., Hou, W. J., Lin, C. S., Tsai, A., Jorstad, S. G., Weaver, Z. R., Acosta-Pulido, J. A., Carnerero, M. I., Carosati, D., Kurtanidze, S. O., Kurtanidze, O. M., Jordan, B., Ivanidze, R. Z., Gazeas, K., Vrontaki, K., Hovatta, T., Liodakis, I., Readhead, A. C. S., Kiehlmann, S., Zheng, W., Filippenko, A. V.

Publikováno v: A&A 682, A114 (2024)

The BL Lac 1ES 2344+514 is known for temporary extreme properties (e.g., a shift of the synchrotron SED peak energy $\nu_{synch,p}$ above 1keV). While those extreme states were so far observed only during high flux levels, additional multi-year obser

Externí odkaz: http://arxiv.org/abs/2310.03922

MuSFA: Improving Music Structural Function Analysis with Partially Labeled Data

Autor: Wang, Ju-Chiang, Smith, Jordan B. L., Hung, Yun-Ning

Music structure analysis (MSA) systems aim to segment a song recording into non-overlapping sections with useful labels. Previous MSA systems typically predict abstract labels in a post-processing step and require the full context of the song. By con

Externí odkaz: http://arxiv.org/abs/2211.15787

To catch a chorus, verse, intro, or anything else: Analyzing a song with structural functions

Autor: Wang, Ju-Chiang, Hung, Yun-Ning, Smith, Jordan B. L.

Conventional music structure analysis algorithms aim to divide a song into segments and to group them with abstract labels (e.g., 'A', 'B', and 'C'). However, explicitly identifying the function of each segment (e.g., 'verse' or 'chorus') is rarely a

Externí odkaz: http://arxiv.org/abs/2205.14700

Supervised Metric Learning for Music Structure Features

Autor: Wang, Ju-Chiang, Smith, Jordan B. L., Lu, Wei-Tsung, Song, Xuchen

Music structure analysis (MSA) methods traditionally search for musically meaningful patterns in audio: homogeneity, repetition, novelty, and segment-length regularity. Hand-crafted audio features such as MFCCs or chromagrams are often used to elicit

Externí odkaz: http://arxiv.org/abs/2110.09000