Download Flutter Echo Modeling Flutter echo is a well-known acoustic phenomenon that occurs when sound waves bounce between two parallel reflective surfaces, creating a repetitive sound. In this work, we introduce a method to recreate flutter echo as an audio effect. The proposed algorithm is based on a feedback structure utilizing velvet noise that aims to synthesize the fluttery components of a reference room impulse response presenting flutter echo. Among these, the repetition time defines the length of the delay line in a feedback filter. The specific spectral properties of the flutter are obtained with a bandpass attenuation filter and a ripple filter, which enhances the harmonic behavior of the sound. Additional temporal shaping of a velvet-noise filter, which processes the output of the feedback loop, is performed based on the properties of the reference flutter. The comparison between synthetic and measured flutter echo impulse responses shows good agreement in terms of both the repetition time and reverberation time values.
Download Differentiable Feedback Delay Network for Colorless Reverberation Artificial reverberation algorithms often suffer from spectral coloration, usually in the form of metallic ringing, which impairs the perceived quality of sound. This paper proposes a method to reduce the coloration in the feedback delay network (FDN), a popular artificial reverberation algorithm. An optimization framework is employed entailing a differentiable FDN to learn a set of parameters decreasing coloration. The optimization objective is to minimize the spectral loss to obtain a flat magnitude response, with an additional temporal loss term to control the sparseness of the impulse response. The objective evaluation of the method shows a favorable narrower distribution of modal excitation while retaining the impulse response density. The subjective evaluation demonstrates that the proposed method lowers perceptual coloration of late reverberation, and also shows that the suggested optimization improves sound quality for small FDN sizes. The method proposed in this work constitutes an improvement in the design of accurate and high-quality artificial reverberation, simultaneously offering computational savings.
Download RIR2FDN: An Improved Room Impulse Response Analysis and Synthesis This paper seeks to improve the state-of-the-art in delay-networkbased analysis-synthesis of measured room impulse responses (RIRs). We propose an informed method incorporating improved energy decay estimation and synthesis with an optimized feedback delay network. The performance of the presented method is compared against an end-to-end deep-learning approach. A formal listening test was conducted where participants assessed the similarity of reverberated material across seven distinct RIRs and three different sound sources. The results reveal that the performance of these methods is influenced by both the excitation sounds and the reverberation conditions. Nonetheless, the proposed method consistently demonstrates higher similarity ratings compared to the end-to-end approach across most conditions. However, achieving an indistinguishable synthesis of measured RIRs remains a persistent challenge, underscoring the complexity of this problem. Overall, this work helps improve the sound quality of analysis-based artificial reverberation.
Download Differentiable Active Acoustics - Optimizing Stability via Gradient Descent Active acoustics (AA) refers to an electroacoustic system that actively modifies the acoustics of a room. For common use cases, the number of transducers—loudspeakers and microphones—involved in the system is large, resulting in a large number of system parameters. To optimally blend the response of the system into the natural acoustics of the room, the parameters require careful tuning, which is a time-consuming process performed by an expert. In this paper, we present a differentiable AA framework, which allows multi-objective optimization without impairing architecture flexibility. The system is implemented in PyTorch to be easily translated into a machine-learning pipeline, thus automating the tuning process. The objective of the pipeline is to optimize the digital signal processor (DSP) component to evenly distribute the energy in the feedback loop across frequencies. We investigate the effectiveness of DSPs composed of finite impulse response filters, which are unconstrained during the optimization. We study the effect of multiple filter orders, number of transducers, and loss functions on the performance. Different loss functions behave similarly for systems with few transducers and low-order filters. Increasing the number of transducers and the order of the filters improves results and accentuates the difference in the performance of the loss functions.