It is not uncommon to hear musicians and audio engineers speak of warmth and brightness when describing analog technologies such as vintage mixing consoles, multitrack tape machines, and valve compressors. What is perhaps less common, is hearing this term used in association with retro digital technology. A question exists as to how much the low bit rate and low-grade conversion quality contribute to the overall brightness or warmth of a sound when processed with audio effects simulating early sampling technologies. These two dimensions of timbre are notoriously difficult to define and more importantly, measure. We present a subjective user study of brightness and warmth, where a series of audio examples are processed with different audio effects. 26 participants rated the perceived level of brightness and warmth of various instrumental sequences for 5 different audio effects including bit depth reduction, compression and equalisation. Results show that 8 bit reduction tends to increase brightness and decrease warmth whereas 12 bit reduction tends to do the opposite, although this is very much dependent on the instrument. Interestingly, the most significant brightness changes, due to bit reduction, were obtained for bass sounds. For comparison purposes, instrument phrases were also processed with both an analogue compressor and an equalisation plugin to see if any subjective difference was noticed when simulating sonic characteristics that might be associated with warmth. Greater significance was observed when the sound excerpts were processed with the plugins being used to simulate the effects of bit depth reduction. CCS Concepts: • Information systems → Speech / audio search; • Applied computing → Sound and music computing; Media arts; • Computing methodologies → Perception; • Software and its engineering → Semantics.

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Music producers and casual users often seek to replicate dynamic range compression used in a particular recording or production context for their own track. However, not knowing the parameter settings used to produce the audio using the effect may become an impediment, especially for beginners or untrained users who may lack critical listening skills. We address this issue by presenting an automatic compressor plugin relying on a neural network to extract relevant features from a reference signal and estimate compression parameters. The plugin automatically adjusts its parameters to match the input signal with a reference audio recording as closely as possible. Quantitative and qualitative usability evaluation of the plugin was conducted with amateur, pro-amateur and professional music producers. The results established acceptance of the core idea behind the proposed control method across these user groups.