# Constraint-Based Time-Scale Modification of Music Recordings for Noise Beautification
## 2 Background
However, this patent does not show how an appropriate music piece for each of the many possible clinical MRI sequences (having different repetition periods) can be found--not to speak of different musical tastes of the patients. To deal with this problem, [PERSON] [7] suggests to use a virtual music composer, which uses audio processing techniques to determine a song's melody, beat, tempo, rhythm, pitches, chords, sonic brilliance, timbre, and several other descriptors. Based on a multidimensional model of the song and of the expected MRI noise, the software synthesizes a new version of the song to match the noise. The goal is to rhythmically and harmonically mask the MRI acoustic noise, thus protecting the hearing organs by leveraging the middle ear reflex. Additionally, a patient's specific musical preferences (such as genre, style, or language) as well as other data relevant to the patient's age, ethnicity, and nationality can be taken into consideration. Similar to the synthesis approach described in [7], our approach modifies a music recording to satisfies constraints that are imposed by the MRI scanner's acoustic noise. In our MRI application scenario, we assume that the MRI scanner's knocking sound patterns are known beforehand. This is a realistic assumption since there is typically a limited number of different MRI scanner settings, which are left fixed during a recording session (see [4]). In many recording situations, each such setting yields a well-defined and predictable sequence of knocking sounds (including the knowledge about the periods of silence). Therefore, one can perform the music adaptation of each of the possible settings in an offline fashion. In the actual noise beautification application, one then only needs to derive a global offset to sync the noise signal with the (precomputed) modified music signal chosen according to the respective MRI setting. The general task of aligning and synchronizing two sound signals is not novel and plays an important role in a wide range of scenarios. For example, music producers or DJs adapt the time-scale of different music recordings in a beat-synchronous fashion for music remixing purposes for creating smooth transitions from one song to another song [8; 9; 10]. Another conceptually related application is proposed in [11; 12], where the goal is to adapt the tempo of the music signal according to an external reference signal such as the steps of a walker or runner. Also in the production of sound tracks for movies, video games, commercials, or audio books, automated tools for adapting existing music recordings to fulfill certain temporal constraints have been studied (see, e.g., [13; 14; 15; 16; 17]). The pointers to the literature are not meant to be comprehensive and only yield some example scenarios. In all these applications, the common goal is to align specific time positions of two different time series under certain constraints. We hope that the specific alignment problem considered in this paper, the mathematical modeling, and the algorithms proposed are applicable and inspirational for other scenarios beyond our motivating MRI scenario.