Artículos de conferencia

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    Stochastic resonance: molding sounds from noise
    (Sound and Music Computing Network, 2024) Gutiérrez, Esteban; Cadiz Cadiz, Rodrigo Fernando
    This paper explores the application of stochastic resonance, a phenomenon known for enhancing signal detection through the introduction of random noise or disorder, to sound synthesis and processing. We detail the various models of stochastic resonance described in the literature and introduce a software implementation as an external for Max/MSP. Through a series of examples, including sound synthesis and processing, reducing the signal-to-noise ratio of noisy sounds, and retrieving the missing fundamental, we demonstrate the efficacy and versatility of this approach in molding sounds from noise.
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    Spectral Stochastic Resonance Sound Synthesis
    (2010) Cadiz Cadiz, Rodrigo Fernando; Cuadra , Patricio de la
    A spectral sound synthesis algorithm based on threshold stochastic resonance is proposed theoretically and implemented in Matlab. The technique consists in the addition of a noise signal in the frequency domain and a comparison with a threshold. By adjusting the standard deviation of the added noise and the threshold factor it is possible to control the sonic features of the synthesized sound. This technique can be applied to both stationary and non-stationary sounds.
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    Reinforcement Learning Based Whale Optimizer
    (Springer International, 2021) Becerra Rozas, Marcelo; Lemus Romani, José Isaac; Crawford, Broderick; Soto, Ricardo; Cisternas Caneo, Felipe; Embry, Andres Trujillo; Molina, Maximo Arnao; Tapia, Diego; Castillo, Mauricio; Misra, Sanjay; Rubio, Jose Miguel
    This work proposes a Reinforcement Learning based optimizer integrating SARSA and Whale Optimization Algorithm. SARSA determines the binarization operator required during the metaheuristic process. The hybrid instance is applied to solve benchmarks of the Set Covering Problem and it is compared with a Q-learning version, showing good results in terms of fitness, specifically, SARSA beats its Q-Learning version in 44 out of 45 instances evaluated. It is worth mentioning that the only instance where it does not win is a tie. Finally, thanks to graphs presented in our results analysis we can observe that not only does it obtain good results, it also obtains a correct exploration and exploitation balance as presented in the referenced literature.
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    A New Learnheuristic: Binary SARSA - Sine Cosine Algorithm (BS-SCA)
    (Springer Science and Business Media Deutschland GmbH, 2022) Becerra-Rozas, Marcelo; Lemus Romani, José Isaac; Crawford, Broderick; Soto, Ricardo; Cisternas Caneo, Felipe; Trujillo Embry, Andrés; Arnao Molina, Máximo; Tapia, Diego; Castillo, Mauricio; Rubio, José Miguel
    This paper proposes a novel learnheuristic called Binary SARSA - Sine Cosine Algorithm (BS-SCA) for solving combinatorial problems. The BS-SCA is a binary version of Sine Cosine Algorithm (SCA) using SARSA to select a binarization operator. This operator is required due SCA was created to work in continuous domains. The performance of BS-SCA is benchmarked with a Q-learning version of the learnheuristic. The problem tested was the Set Covering Problem and the results show the superiority of our proposal.
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    Una mirada de inclusión social y productiva para la región del Maule
    (2019) Cerecera Cabalin, Francisco Esteban; Ojeda Fuentes, Betsy; Morales Arenas, Daniela; Castillo Cortés, Miguel