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Category: media & arts – Page 23

When we listen to a song or musical performance, out-of-tune singers or instruments are generally perceived as unpleasant for listeners. While it is well-established that mistuning can reduce the enjoyment of music, the processes influencing how humans perceive mistuning have not yet been fully delineated.

Researchers at the University of Minnesota recently carried out a study aimed at better understanding factors influencing the extent to which individuals can perceive mistuning in natural music. Their findings, published in Communications Psychology, highlight acoustic elements that influence the perception of dissonance when hearing out-of-tune singing voices or instruments.

“An out-of-tune singer or instrument can ruin the enjoyment of music,” Sara M. K. Madsen and Andrew J. Oxenham wrote in their paper. “However, there is disagreement on how we perceive mistuning in natural music settings. To address this question, we presented listeners with in-tune and out-of-tune passages of two-part music and manipulated the two primary candidate acoustic cues: beats (fluctuations caused by interactions between nearby frequency components) and inharmonicity (non-integer harmonic frequency relationships) across seven experiments.”

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In recent years, artificial intelligence (AI) and deep learning models have advanced rapidly, becoming easily accessible. This has enabled people, even those without specialized expertise, to perform various tasks with AI. Among these models, generative adversarial networks (GANs) stand out for their outstanding performance in generating new data instances with the same characteristics as the training data, making them particularly effective for generating images, music, and text.

GANs consist of two , namely, a generator that creates new data distributions starting from random noise, and a discriminator which checks whether the generated data distribution is “real” (matching the training data) or “fake.” As training progresses, the generator improves at generating realistic distributions, and the discriminator at identifying the generated data as fake.

GANs use a loss function to measure differences between the fake and real distributions. However, this approach can cause issues like gradient vanishing and unstable learning, directly impacting stability and efficiency. Despite considerable progress in improving GANs, including structural modifications and loss function adjustments, challenges such as gradient vanishing and mode collapse, where the generator produces a limited variety, continue to limit their applicability.

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Approximately 41 000 years ago, Earth’s magnetic field briefly reversed during what is known as the Laschamp event. During this time, Earth’s magnetic field weakened significantly—dropping to a minimum of 5% of its current strength—which allowed more cosmic rays to reach Earth’s atmosphere.

Scientists at the Technical University of Denmark and the German Research Centre for Geosciences used data from ESA’s Swarm mission, along with other sources, to create a sounded visualisation of the Laschamp event. They mapped the movement of Earth’s magnetic field lines during the event and created a stereo sound version which is what you can hear in the video.

The soundscape was made using recordings of natural noises like wood creaking and rocks falling, blending them into familiar and strange, almost alien-like, sounds. The process of transforming the sounds with data is similar to composing music from a score.

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