Download Neural Sample-Based Piano Synthesis Piano sound emulation has been an active topic of research and development for several decades. Although comprehensive physicsbased piano models have been proposed, sample-based piano emulation is still widely utilized for its computational efficiency and
relative accuracy despite presenting significant memory storage
requirements. This paper proposes a novel hybrid approach to
sample-based piano synthesis aimed at improving the fidelity of
sound emulation while reducing memory requirements for storing samples. A neural network-based model processes the sound
recorded from a single example of piano key at a given velocity.
The network is trained to learn the nonlinear relationship between
the various velocities at which a piano key is pressed and the corresponding sound alterations. Results show that the method achieves
high accuracy using a specific neural architecture that is computationally efficient, presenting few trainable parameters, and it requires memory only for one sample for each piano key.
Download A Virtual Tube Delay Effect A virtual tube delay effect based on the real-time simulation of acoustic wave propagation in a garden hose is presented. The paper describes the acoustic measurements conducted and the analysis of the sound propagation in long narrow tubes. The obtained impulse responses are used to design delay lines and digital filters, which simulate the propagation delay, losses, and reflections from the end of the tube which may be open, closed, or acoustically attenuated. A study on the reflection caused by a finite-length tube is described. The resulting system consists of a digital waveguide model and produces delay effects having a realistic low-pass filtering. A stereo delay effect plugin in P URE DATA1 has been implemented and it is described here.
Download Deep Learning Conditioned Modeling of Optical Compression Deep learning models applied to raw audio are rapidly gaining relevance in modeling audio analog devices. This paper investigates the use of different deep architectures for modeling audio optical compression. The models use as input and produce as output raw audio samples at audio rate, and it works with noor small-input buffers allowing a theoretical real-time and lowlatency implementation. In this study, two compressor parameters, the ratio, and threshold have been included in the modeling process aiming to condition the inference of the trained network. Deep learning architectures are compared to model an all-tube optical mono compressor including feed-forward, recurrent, and encoder-decoder models. The results of this study show that feedforward and long short-term memory architectures present limitations in modeling the triggering phase of the compressor, performing well only on the sustained phase. On the other hand, encoderdecoder models outperform other architectures in replicating the overall compression process, but they overpredict the energy of high-frequency components.