Thomas Prätzlich

Kernel Additive Modeling for interference reduction in multi-channel music recordings

When recording a live musical performance, the different voices, such as the instrument groups or soloists of an orchestra, are typically recorded in the same room simultaneously, with at least one microphone assigned to each voice. However, it is difficult to acoustically shield the microphones. In practice, each one contains interference from every other voice. In this paper, we aim to reduce these interferences in multi-channel recordings to recover only the isolated voices. Following the recently proposed Kernel Additive Modeling framework, we present a method that iteratively estimates both the power spectral density of each voice and the corresponding strength in each microphone signal. With this information, we build an optimal Wiener filter, strongly reducing interferences. The trade-off between distortion and separation can be controlled by the user through the number of iterations of the algorithm. Furthermore, we present a computationally effective approximation of the iterative procedure. Listening tests demonstrate the effectiveness of the method.

Score-informed audio decomposition and applications

The separation of different sound sources from polyphonic music recordings constitutes a complex task since one has to account for different musical and acoustical aspects. In the last years, various score-informed procedures have been suggested where musical cues such as pitch, timing, and track information are used to support the source separation process. In this paper, we discuss a framework for decomposing a given music recording into notewise audio events which serve as elementary building blocks. In particular, we introduce an interface that employs the additional score information to provide a natural way for a user to interact with these audio events. By simply selecting arbitrary note groups within the score a user can access, modify, or analyze corresponding events in a given audio recording. In this way, our framework not only opens up new ways for audio editing applications, but also serves as a valuable tool for evaluating and better understanding the results of source separation algorithms.

Memory-restricted multiscale dynamic time warping

Dynamic Time Warping (DTW) is an established method for finding a global alignment between two feature sequences. However, having a computational complexity that is quadratic in the input length, memory consumption becomes a major issue when dealing with long feature sequences. Various strategies have been proposed to reduce the memory requirements of DTW. For example, online alignment approaches often have a constant memory consumption by applying forward path estimation strategies. However, this comes at the cost of robustness. Efficient offline DTW based on multiscale strategies constitutes another approach. While methods built on this principle are usually robust, their memory requirements are still dependent on the input length. By combining ideas from online alignment approaches and offline multiscale strategies, we introduce a novel alignment procedure that allows for specifying a constant upper bound on its memory requirements. This is an important aspect when working on devices with limited computational resources. Experiments show that when restricting the memory consumption of our proposed procedure to eight megabytes, it basically yields the same alignments as the standard DTW procedure.

Known-Artist Live Song Identification Using Audio Hashprints

The goal of live song identification is to allow concertgoers to identify a live performance by recording a few seconds of the performance on their cell phone. This paper proposes a multistep approach to address this problem for popular bands. In the first step, GPS data are used to associate the audio query with a concert in order to infer who the musical artist is. This reduces the search space to a dataset containing the artists studio recordings. In the next step, the known-artist search is solved by representing the audio as a sequence of binary codes called hashprints, which can be efficiently matched against the database using a two-stage cross-correlation approach. The hashprint representation is derived from a set of spectrotemporal filters that are learned in an unsupervised artist-specific manner. On the Gracenote live song identification benchmark, the proposed system outperforms five other baseline systems and improves the mean reciprocal rank of the previous state of the art from 0.68 to 0.79, while simultaneously reducing the average runtime per query from 10 to 0.9 s. We conduct extensive analyses of major factors affecting system performance.

Triple-based analysis of music alignments without the need of ground-truth annotations

The goal of music alignment methods is to temporally align different versions of the same piece of music. These methods are typically evaluated by comparing the computed alignments to given ground-truth annotations. Creating such annotations is usually very labor intensive. For many musical pieces, especially in classical music, there exists a multitude of different recordings. In this work, we investigate whether an evaluation of music alignment algorithms can be performed without ground-truth annotations when at least a triplet of recordings of the same piece of music is available. The main idea is to align the time points of a fixed reference version, in a circular way, back through a second and third version by using their pairwise alignments. A triple error is then computed by comparing these time points with their circularly aligned version. In this paper, we formalize the idea of the triple error and discuss its potential and limitations. We present typical examples for the triple error and compare it to the pairwise alignment error based on ground-truth. Furthermore, we present a case study to indicate the potential of the triple error to analyze alignments and to compare different alignment methods without the need of ground-truth annotations.