Real world music object recognition

We present solutions to two of the most pressing issues in contemporary optical music recognition (OMR).We improve recognition accuracy on low-quality, real-world (i.e. containing ageing, lighting, or dirt artefacts among others) input data and provide confidence-rated model outputs to enable efficient human post-processing. Specifically, we present (i) a sophisticated input augmentation scheme that can reduce the gap between sanitised benchmarks and realistic tasks through a combination of synthetic data and noisy perturbations of real-world documents; (ii) an adversarial discriminative domain adaptation method that can be employed to improve the performance of OMR systems on low-quality data; (iii) a combination of model ensembles and prediction fusion, which generates trustworthy confidence ratings for each prediction. We evaluate our contributions on a newly created test set consisting of manually annotated pages of varying real-world quality, sourced from International Music Score Library Project (IMSLP) / the Petrucci Music Library. With the presented data augmentation scheme, we achieve a doubling in detection performance from 36.0% to 73.3% on noisy real-world data compared to state-of-the-art training. This result is then combined with robust confidence ratings paving the way forOMR to be deployed in the realworld. Additionally, we showthe merits of unsupervised adversarial domain adaptation for OMR raising the 36.0% baseline to 48.9%. All our code and data are freely available at: https://github.com/raember/s2anet/tree/TISMIR_publication

Evaluating the key assumptions underlying dendro-provenancing: How to spruce it up with a scissor plot

In this paper, dendro-provenancing is framed as a search for statistical Nearest Neighbors. The ‘k-Nearest Neighbors leave one-out cross-validation’ process (k-NN) is proposed as a method for validating dendro-provenancing approaches. Furthermore, it allows researchers to consistently compare and evaluate different proximity measures with respect to their suitability for dendro-provenancing. The validation process is demonstrated on a data set of 401 ring-width series of Norway spruce (Picea abies (L.) H. Karst.) encompassing 15 sites along elevational gradients in north-eastern Switzerland. Moreover, a new type of plot, the so-called scissor plot, is introduced to visualize the k-NN validation process. Results indicate that dendro-provenancing depends heavily on differences in between sites high-frequencysignal. Mean classification success for the relevant stages of the k-NN (CS¯Ropen)1 ranged from 71.8% to 79.2% for the best performing measures. Classification errors occurred mainly between sites at elevations of 1000–1198m a.s.l. At all other elevations and between different regions of the study area, only moderate differences in classification performance were detected. Thus, the results indicate that dendro-provenancing may be principally feasible even in a small region as studied here

Assessing site signal preservation in reference chronologies for dendro-provenancing

Regional differences in tree growth can be used to approximate the geographical provenance of ring-width series (‘dendro-provenancing’). This method relies on cross-dated ring-width series (reference chronologies) that are thought to represent the radial growth signal of trees in a given region. Reference chronologies are often established from ring-width series of living tree populations. Frequently, they are too short to allow for investigating the provenance of historical wood. Thus, references are extended by ring-width series from buildings and art-historical objects that exhibit best matching growth patterns with the living tree references. Yet, series from other provenances may erroneously be included. Thereby the local or regional growth signal of the references is progressively contaminated, but this has received little attention to date. I investigate this contamination risk using a simulation approach that allows for generating pseudo site chronologies that preserve the relevant statistical properties of the real site chronologies. While the exact provenance of historical wood is unknown, for simulated ring-width series the provenance is unambiguous. Hence, pseudo reference chronologies may be established while monitoring the signal mixture. Specifically, 15 site chronologies of Norway spruce (Picea abies (L.) H. Karst.) from northeastern Switzerland were used to generate 15 pseudo site growth signals that span 1000 years. The simulation demonstrates that quasi uncontaminated references can be established in ideal circumstances for the study area. However, the thresholds for the similarity in between-series correlation must be very high. Even then, contaminated pseudo references occurred in rare cases during the simulation. Yet, elevation-specific pseudo references were established with lower thresholds. Simulation currently offers the only approach for assessing the contamination risk of reference chronologies, and it allows for elucidating the conditions under which acceptable levels of contamination can be guaranteed. Therefore, the present approach paves the way towards a practical simulation tool for dendro-provenancing.ISSN:1932-620