Piano Concerto Dataset (PCD): A Multitrack Dataset of Piano Concertos

Abstract

The piano concerto is a genre of central importance in Western classical music, often consisting of a virtuoso solo part for piano and an orchestral accompaniment. In this article, we introduce the Piano Concerto Dataset (PCD), which comprises a collection of excerpts with separate piano and orchestral tracks from piano concertos ranging from the Baroque to the Post-Romantic era. In particular, using existing backing tracks by the music publisher Music Minus One, we recorded excerpts from 15 different piano concertos played by five interpreters on various instruments under different acoustic conditions. The key challenge of playing along with pre-recorded orchestral accompaniments lies in the exact synchronization of the performer. For guiding the pianists for obtaining a high synchronization accuracy, we used additional click tracks generated with measure and beat annotations of the orchestral tracks, which also are provided in the PCD. The dataset is relevant for a variety of Music Information Retrieval (MIR) tasks, including music source separation, automatic accompaniment, music synchronization, editing, and upmixing.

Source Separation of Piano Concertos Using Musically Motivated Augmentation Techniques

“…Supervised deep learning models designed for MSS typically rely on large datasets containing recordings of isolated stems. Since such multi-track recordings are not available in the case of piano concertos, we create a dataset as in our previous work [2] through random mixes of piano-only recordings (e.g., piano sonatas) and recordings of orchestral music without piano (e.g., symphonies), see Figure 3a for an illustration. While this method does not reflect the harmonic and rhythmic interaction among different instruments found in most real recordings, it helps the MSS model identify the timbral characteristics of concurrent musical sources.…”

“…Moreover, piano concertos often comprise long piano-only (e.g., in the cadenza) and orchestra-only parts (e.g., in the exposition, also called opening ritornello). Our previous work [2] exploits this property of the piano concertos for further finetuning the MSS model at test time, a strategy called test-time adaptation [74]. Several works in the literature apply activity-based approaches as a prior to enhance audio source separation, e.g., [75], [76].…”

“…Although practicing and playing piano concertos is a main activity of pianists in their career, only first-class pianists get the opportunity to perform alongside an orchestra. Motivated by the need for orchestral accompaniments of amateur or semiprofessional pianists, we consider the novel task of separating piano concertos building on our previous work [2], which we substantially extend in this paper, particularly through the adaptation of four deep learning (DL) models. For an illustration of the task, see Figure 1.…”

“…In case multi-track recordings are unavailable, random mixing can be used to artificially generate and augment training data [10], [27]. Following this strategy, we used artificial training material in a previous work [2] by randomly mixing sections selected from the solo piano repertoire (e.g., piano sonatas, etudes, etc.) and orchestral pieces without piano (e.g., symphonies) to train an MSS model based on Spleeter [5].…”

“…As another extension of [2], instead of using artificially generated test data, we evaluate our models using the Piano Concerto Dataset (PCD) [28], which provides a wide range of piano concerto recordings played by five performers in four different acoustic environments. For the evaluation of our models' performance, we use the widely-used Signal to Distortion Ratio (SDR) [29] and also the 2f-score [30], which is a perceptually motivated quality measure yielding better results in source separation tasks [31].…”