Abstract
Chord progressions play an important role in Western tonal music. For a novice composer, the creation of chord progressions can be challenging because it involves many subjective factors, such as the musical context, personal preference and aesthetic choices. This work proposes ChordAIS, an interactive system that assists the user in generating chord progressions by iteratively adding new chords. At each iteration a search for the next candidate chord is performed in the Tonal Interval Space (TIS), where distances capture perceptual features of pitch configurations on different levels, such as musical notes, chords, and scales. We use an artificial immune system (AIS) called opt-aiNet to search for candidate chords by optimizing an objective function that encodes desirable musical properties of chord progressions as distances in the TIS. Opt-aiNet is capable of finding multiple optima of multi-modal functions simultaneously, resulting in multiple good-quality candidate chords which can be added to the progression by the user. To validate ChordAIS, we performed different experiments and a listening test to evaluate the perceptual quality of the candidate chords proposed by ChordAIS. Most listeners rated the chords proposed by ChordAIS as better candidates for progressions than the chords discarded by ChordAIS. Then, we compared ChordAIS with two similar systems, ConChord and ChordGA, which uses a standard GA instead of opt-aiNet. A user test showed that ChordAIS was preferred over ChordGA and Conchord. According to the results, ChordAlS was deemed capable of assisting the users in the generation of tonal chord progressions by proposing good-quality candidates in all the keys tested.

Abstract
The aim of computer-aided musical orchestration (CAMO) is to find a combination of musical instrument sounds that perceptually approximates a reference sound when played together. The complexity of timbre perception and the combinatorial explosion of all possible musical instrument sound combinations make it very challenging to find even one orchestration for a reference sound. However, finding only one orchestration is seldom enough given the creative nature of the compositional process. Compositional applications of computer-aided musical orchestration can greatly benefit from multiple orchestrations with diversity. In this work, we use an artificial immune system (AIS) called opt-aiNet to search for combinations of musical instrument sounds that minimize the distance to a reference sound encoded in a fitness function. Opt-aiNet was developed to maximize diversity in the solution set of multi-modal optimization problems, which results in multiple alternative orchestrations for the same reference sound that are different among themselves. We compared the diversity and the similarity of the orchestrations proposed by opt-aiNet (CAMO-AIS) against a standard genetic algorithm (CAMO-GA) and Orchids, which is considered the state of the art for CAMO, for 13 reference sounds. In general, CAMO-AIS outperformed CAMO-GA and Orchids for several measures of objective diversity. We performed a listening test to evaluate and compare the perceptual similarity of the orchestrations by CAMO-AIS and Orchids. CAMO-AIS generated orchestrations that were perceived to be as similar to the reference sounds as those returned by Orchids. Therefore, CAMO-AIS has higher diversity of orchestrations than Orchids without loss of perceptual similarity.

Abstract
The automatic segmentation of isolated musical instrument sounds according to the temporal evolution is not a trivial task. It requires a model capable of capturing regions such as the attack, decay, sustain and release accurately for many types of instruments with different modes of excitation. The traditional ADSR amplitude envelope model does not apply universally to acoustic musical instrument sounds with different excitation methods because it uses strictly amplitude information and supposes all sounds manifest the same temporal evolution. We present an automatic segmentation technique based on a more realistic model of the temporal evolution of many types of acoustic musical instruments that incorporates both temporal and spectrotemporal cues. The method allows a robust and more perceptually relevant automatic segmentation of the isolated sounds of many musical instruments that fit the model.

Abstract
The aim of sound morphing is to obtain a sound that falls perceptually between two (or more) sounds. Ideally, we want to morph perceptually relevant features of sounds and be able to independently manipulate them. In this work we present a method to obtain perceptually intermediate spectral envelopes guided by highlevel spectral shape descriptors and a technique that employs evolutionary computation to independently manipulate the timbral features captured by the descriptors. High-level descriptors are measures of the acoustic correlates of salient timbre dimensions derived from perceptual studies, such that the manipulation of the descriptors corresponds to potentially interesting timbral variations.

Abstract
We present a time domain approach to explore a sound transformation paradigm for musical performance. Given a set of sounds containing a priori desired qualities and a population of agents interacting locally, the method generates both musical form and matter resulting from sonic trajectories. This proposal involves the use of bio-inspired algorithms, which possess intrinsic features of adaptive, self-organizing systems, as definers of generating and structuring processes of sound elements. Self-organization makes viable the temporal emergence of stable structures without an external organizing element. Regarding musical performance. as a creative process that can be described using trajectories through the compositional space, and having the simultaneous emergence of musical matter and form resulting from the process itself as the final objective, the conception of a generative paradigm in computer music that does not contemplate a priori external organizing elements is the main focus of this proposal.

Abstract
Computer generated sounds for music applications have many facets, of which timbre design is of groundbreaking significance. Timbre is a remarkable and rather complex phenomenon that has puzzled researchers for a long time. Actually, the nature of musical signals is not fully understood yet. In this paper, we present a sound synthesis method using an artificial immune network for data clustering, denoted aiNet. Sounds produced by the method are referred to as immunological sounds. Basically, antibody-sounds are generated to recognize a fixed and predefined set of antigen-sounds, thus producing timbral variants with the desired characteristics. The aiNet algorithm provides maintenance of diversity and an adaptive number of resultant antibody-sounds (memory cells), so that the intended aesthetical result is properly achieved by avoiding the formal definition of the timbral attributes. The initial set of antibody-sounds may be randomly generated vectors, sinusoidal waves with random frequency, or a set of loaded waveforms. To evaluate the obtained results we propose an affinity measure based on the average spectral distance from the memory cells to the antigen-sounds. With the validation of the affinity criterion, the experimental procedure is outlined, and the results are depicted and analyzed.