Algorithmic and Intelligent Instruments

‘Machine listening’ implemented by means of audio analysis finds an interesting partner in algorithmic composition: both appear to merge as a virtual musician. However, the algorithms appear also in separate software. Chadabe claims that the processes of M149_, “partly based on random number generation, simulate the complexity of improvisation. To whatever extent you direct it to do so, and in whatever way you direct it, M improvises on your musical material.”150 _{The role of randomness is essentially a means} to implement “elusive causes” which can inspire and give unexpected and new direction to the musical outcomes. “It might be a bit unusual to associate randomness and intuition, but they are both the results of underlying and elusive causes.”151 _The translation of the randomness into rule based processes has attracted considerable research for possibilities to merge musical expectation with an element of freshness and

149 _{First published by Intelligent Music in 1987, no available from Cycling 74. http:// cycling74.com/}

products/m/ last visited 20.07.2012.

150 _{Chadabe 1991, 143.} 151 _{Chadabe 1991, 143.}

surprise. Each project differs in approach and intend but nevertheless there is an element to establish an autonomy of the system, which eventually results in a virtual musician. Biles’Gen Jam152 _{and Lewis’ Voyager}153_{, both generative algorithms capable} to accompany a live musician, have a very different result. While Biles created a very sophisticated accompanying partner trained in a selection of music genres, Voyager is embodying an encoded version of Lewis’ understanding of jazz and improvisation. Both algorithmic systems calculate performance data about pitch, rhythm and dynamics, which are then rendered by MIDI capable instruments or synthesisers. This is of significance as in this way the algorithm deal with the material as if generating a real- time score. Edwards also points out his generative composition software slippery

chicken154 _{to have reached “the stage where it can generate, in a single pass, complete}

musical scores for traditional instruments or with the same data write sound files using samples or MIDI file realizations of the instrumental score”155_.

Continuing advances in algorithmic and generative software technology shows that possible solutions can include audio analysis to create sophisticated sound dependent relationships between live instruments and sampled audio in real-time156_{. Diemo} Schwarz’s CataRT157_{, Michael Casey’s Soundspotter}158_{, and David Casal’s combination} of Casey’s MPEG-7 technology and co-evolutionary algorithms in Frank159 _are impressive examples of synthesising the electroacoustic accompaniment for an acoustic instrument from fragments prerecorded and analysed audio files. A real-time analysis of an audio stream can be utilised to establish close associations between live and the retrieval of sampled material, i.e. in form of material with the closest match of descriptors stored in the database. It has been an interesting experience to hear Casal’s piano performance160_{, where music by Anthony Braxton, Cecil Taylor and Ligeti} 152 _{Biles 1994.}

153 _{Lewis 2000.} 154 _{Edwards 2000.} 155 _{Edwards 2012, 64.}

156 _{An overview of the research involving Music Information Retrieval is comprehensively given by}

Downie (2008). The proceedings of the dedicated conference International Society for Music Information Retrieval (ISMIR) indicate in particular the scale and variety of work in this field. While its techniques and outcomes yield much relevance and interest to this thesis in general terms, the research and implementations of these methods within the available performance opportunities and venues proved impractical.

157 _{Schwarz 2006.} 158 _{Casey 2002.} 159 _{Casal 2007.}

became the orchestral accompaniment to the solo piano. The analysis of the musical gestures of the solo piano was used to retrieve short fragments out of the database compiled of these recordings. It is an example of consequent computer control derived from the music itself: the computer playback follows the piano performance step by step, so that a peculiar parity in musical structure emerges between the instrumental and electronic part. Casal’s performance remains locked within a continuous stream of musical references and ‘anecdotes’ in forms of fleeting impressions from the material stored in its database. It moves within an aesthetic of de- and reconstruction: it plays with memory and time perception, reminiscent of Oswald’s Plunderphonics, despite its real-time properties. Casal’s work is considered very significant as he has achieved a direct link to the electronic part of the performance without relying on direct control methods and in absence of implemented fundamental musical rules. Whereas Lewis’

Voyager is based on meticulous implementation of harmonic, rhythmic and motific

possibilities, Frank works with real-time audio analysis and pre-analysed audio recordings. Therefore the ‘musical language’ is not determined by the musical rules, but by the audio resources used. Casal’s system reacts entirely differently when loaded with different recordings despite employing the same algorithmic links. The associations of the timbre between the performance and electronic response will be perceived differently. For example, trills would not be accompanied by dense textures of the Ligeti if the database does not contain material which features fast changes in pitches. Strong links are created between piano and electronics at the micro-structural level: musical gestures and motifs create clearly perceivable causalities which is suggestive of a musical dialogue between the piano and accompaniment.