Analog electronic circuits are at the core of an important category of
musical devices. The nonlinear features of their electronic components give
analog musical devices a distinctive timbre and sound quality, making them
highly desirable. Artificial neural networks have rapidly gained popularity for
the emulation of analog audio effects circuits, particularly recurrent
networks. While neural approaches have been successful in accurately modeling
distortion circuits, they require architectural improvements that account for
parameter conditioning and low latency response. In this article, we explore
the application of recent machine learning advancements for virtual analog
modeling. We compare State Space models and Linear Recurrent Units against the
more common Long Short Term Memory networks. These have shown promising ability
in sequence to sequence modeling tasks, showing a notable improvement in signal
history encoding. Our comparative study uses these black box neural modeling
techniques with a variety of audio effects. We evaluate the performance and
limitations using multiple metrics aiming to assess the models' ability to
accurately replicate energy envelopes, frequency contents, and transients in
the audio signal. To incorporate control parameters we employ the Feature wise
Linear Modulation method. Long Short Term Memory networks exhibit better
accuracy in emulating distortions and equalizers, while the State Space model,
followed by Long Short Term Memory networks when integrated in an encoder
decoder structure, outperforms others in emulating saturation and compression.
When considering long time variant characteristics, the State Space model
demonstrates the greatest accuracy. The Long Short Term Memory and, in
particular, Linear Recurrent Unit networks present more tendency to introduce
audio artifacts.

通过比较 State Space 模型、Linear Recurrent Units 和 Long Short Term Memory 网络在模拟音频效果方面的性能，本文研究了近期机器学习进展在虚拟类比建模中的应用，包括信号历史编码、能量包络、频率内容和瞬态等方面的准确度。结果表明，Long Short Term Memory 网络在模拟失真和均衡器方面的准确度较高，而 State Space 模型在饱和和压缩方面的模拟能力超过其他方法。对于长时间变化特性，State Space 模型展现了最高的准确度。Long Short Term Memory 网络和 Linear Recurrent Unit 网络则更容易引入音频伪像。

虚拟模拟音频效果建模的循环神经网络比较研究

Comparative Study of Recurrent Neural Networks for Virtual Analog Audio  Effects Modeling

The way the media presents events can significantly affect public perception,
which in turn can alter people's beliefs and views. Media bias describes a
one-sided or polarizing perspective on a topic. This article summarizes the
research on computational methods to detect media bias by systematically
reviewing 3140 research papers published between 2019 and 2022. To structure
our review and support a mutual understanding of bias across research domains,
we introduce the Media Bias Taxonomy, which provides a coherent overview of the
current state of research on media bias from different perspectives. We show
that media bias detection is a highly active research field, in which
transformer-based classification approaches have led to significant
improvements in recent years. These improvements include higher classification
accuracy and the ability to detect more fine-granular types of bias. However,
we have identified a lack of interdisciplinarity in existing projects, and a
need for more awareness of the various types of media bias to support
methodologically thorough performance evaluations of media bias detection
systems. Concluding from our analysis, we see the integration of recent machine
learning advancements with reliable and diverse bias assessment strategies from
other research areas as the most promising area for future research
contributions in the field.

媒体偏见检测是一个活跃的研究领域，基于计算方法和机器学习的分类方法让我们能够更准确地检测和分类各种类型的媒体偏见，然而，现有项目存在学科间的缺乏交叉和对多种媒体偏见类型的认识，未来的研究应该集成最新的机器学习进展和来自其他研究领域的可靠和多样化的偏见评估策略。