Synthesizing performing guitar sound is a highly challenging task due to the
polyphony and high variability in expression. Recently, deep generative models
have shown promising results in synthesizing expressive polyphonic instrument
sounds from music scores, often using a generic MIDI input. In this work, we
propose an expressive acoustic guitar sound synthesis model with a customized
input representation to the instrument, which we call guitarroll. We implement
the proposed approach using diffusion-based outpainting which can generate
audio with long-term consistency. To overcome the lack of MIDI/audio-paired
datasets, we used not only an existing guitar dataset but also collected data
from a high quality sample-based guitar synthesizer. Through quantitative and
qualitative evaluations, we show that our proposed model has higher audio
quality than the baseline model and generates more realistic timbre sounds than
the previous leading work.

通过使用定制化的输入表示方法，我们提出了一种表达丰富的声学吉他声音合成模型，该模型名为 guitarroll。通过扩散外推生成长期一致性的音频，我们的模型在音频质量方面优于基线模型，并比以前的主要工作生成更真实的音色声音。

一种具有乐器特定输入表示和扩散绘制的表达性声学吉他声音合成

Expressive Acoustic Guitar Sound Synthesis with an Instrument-Specific  Input Representation and Diffusion Outpainting

Denoising Diffusion Probabilistic Models (DDPMs) have made great strides in
generating high-quality samples in both discrete and continuous domains.
However, Discrete DDPMs (D3PMs) have yet to be applied to the domain of
Symbolic Music. This work presents the direct generation of Polyphonic Symbolic
Music using D3PMs. Our model exhibits state-of-the-art sample quality,
according to current quantitative evaluation metrics, and allows for flexible
infilling at the note level. We further show, that our models are accessible to
post-hoc classifier guidance, widening the scope of possible applications.
However, we also cast a critical view on quantitative evaluation of music
sample quality via statistical metrics, and present a simple algorithm that can
confound our metrics with completely spurious, non-musical samples.

本文提出了一种使用 Discrete DDPMs (D3PMs) 直接生成 Polyphonic Symbolic Music 的方法，并展示了该模型的高质量和灵活的属性，并警示无法通过量化指标完全评估样本质量。

离散扩散概率模型用于符号音乐生成

Discrete Diffusion Probabilistic Models for Symbolic Music Generation

Polyphonic sound event detection and direction-of-arrival estimation require
different input features from audio signals. While sound event detection mainly
relies on time-frequency patterns, direction-of-arrival estimation relies on
magnitude or phase differences between microphones. Previous approaches use the
same input features for sound event detection and direction-of-arrival
estimation, and train the two tasks jointly or in a two-stage transfer-learning
manner. We propose a two-step approach that decouples the learning of the sound
event detection and directional-of-arrival estimation systems. In the first
step, we detect the sound events and estimate the directions-of-arrival
separately to optimize the performance of each system. In the second step, we
train a deep neural network to match the two output sequences of the event
detector and the direction-of-arrival estimator. This modular and hierarchical
approach allows the flexibility in the system design, and increase the
performance of the whole sound event localization and detection system. The
experimental results using the DCASE 2019 sound event localization and
detection dataset show an improved performance compared to the previous
state-of-the-art solutions.

提出一种模块化和分层的方法，将声音事件检测和到达方向估计的学习分离开来，在利用神经网络匹配这两个输出序列的基础上，实现了对整个声事件定位和检测系统性能的提升。

多声部声音事件定位和检测的序列匹配网络

A Sequence Matching Network for Polyphonic Sound Event Localization and  Detection

Sound event detection (SED) and localization refer to recognizing sound
events and estimating their spatial and temporal locations. Using neural
networks has become the prevailing method for SED. In the area of sound
localization, which is usually performed by estimating the direction of arrival
(DOA), learning-based methods have recently been developed. In this paper, it
is experimentally shown that the trained SED model is able to contribute to the
direction of arrival estimation (DOAE). However, joint training of SED and DOAE
degrades the performance of both. Based on these results, a two-stage
polyphonic sound event detection and localization method is proposed. The
method learns SED first, after which the learned feature layers are transferred
for DOAE. It then uses the SED ground truth as a mask to train DOAE. The
proposed method is evaluated on the DCASE 2019 Task 3 dataset, which contains
different overlapping sound events in different environments. Experimental
results show that the proposed method is able to improve the performance of
both SED and DOAE, and also performs significantly better than the baseline
method.

本文提出基于神经网络的声音事件检测和定位方法，该方法训练得到的声音事件检测模型可以帮助到方位角的估计，同时也能够显著提高声音事件检测和方位角估计的性能。