The process of mixing tracks for a live stage performance or studio session is both time consuming and expensive with assistance of professionals. It is also difficult for individuals to remain competitive against established companies, since multiple tracks must be properly mixed in order to achieve well-enhanced elements -- generally, a poor mix makes it difficult for the listener to distinguish the different elements of the mix. The developed method during this thesis work aims at facilitating the mixing work for live performances and studio sessions. The implemented system analyzes the energy spectrum of the tracks included in the mix. By unmasking spectral components, the spectral overlap of the tracks is minimized. The system filters non-characteristic frequencies, leaving significant frequencies undisturbed. Five tracks have been used from the final mix of a successful radio song. These tracks have been analyzed and used to illustrate and validate the developed method. The system was successfully implemented in MATLAB with promising results and conclusions. The processed mix unmasks frequency content and is perceived to sound clearer compared to the unprocessed mix by a number of test individuals. The method reminds of a multi-band compressor that analyzes the spectral information between tracks. Thus, by use of inter-spectral dependencies, the thesis investigates the possibility to control the amplitudes in time by filtration in frequency domain. The compression rate in time domain is reflected in regard to a trade-off between conservation of characteristic frequencies and reduction of spectral overlaps.

Film audiences have expressed continuous criticism about film sound, claiming that in many situations dialogue is difficult to hear. This issue raises questions about the factors that have negative impact on speech intelligibility and points to investigate strategies that maintain dialogue intelligible in a 5.1. This study investigates how listeners, audio engineering students, experience dialogue intelligibility, when different mixing techniques as dynamic equalization and ducking are applied to the sound effect. The results show that both signal processing techniques, dynamic equalization and ducking, are able to improve dialogue intelligibility and furthermore, dynamic equalization has been proved as the most effective.