## 5 Conclusions  
In this paper, a novel algorithm was proposed for ultra-fast pitch detection for real-time applications, based on a modified autocorrelation implemented on a single-bit signal. The OneBitPitch (OBP) algorithm was compared with the most widely used models for high-speed F0 detection, namely, YIN, SWIPE and an NLS-based implementation. Additionally, a custom dataset made of synthetic waves has been proposed and made available to the public: the SYNTHPTICH dataset encompasses sinusoidal and square waves with added artifacts for an increasing pitch complexity, realized through the addition of harmonics, partials, white noise and reverb.  
OBP is shown to be an ultra-fast, reliable pitch detection algorithm for real-time applications: it has been built with a minimalist approach that aims to reduce all computationally expensive steps in pitch detection, which are mainly represented by a multi-bit elaboration, the transformation/metric production and the eventual presence of corrective heuristics.  
The focus of the OBP algorithm is solely on speed, provided that acceptable accuracy levels are reached, and its characteristics make it exceptionally suitable for an easy and lightweight hardware implementation on FPGA or SoC, for ever-higher customization possibilities as well as lower latencies.  
The comparison of different state-of-the-art algorithms shows that OBP is 9 times faster than the peak speed of the other algorithms (namely, YIN) and 50 times faster than SWIPE. OBP is shown to be the fastest pitch detection algorithm within the presented test, with only 4.6 ms of mean elapsed time on each data instance, or 0.046 \(\times\) realtime runtime, which is the lowest reported to date.  
On the other hand, although relative error might be increased for certain datasets, OBP is less prone to octave errors, and in general demonstrates the ability to stay within the acceptability range on most of the tested signals. The main compromise was between speed and accuracy, and OBP stays within acceptable ranges of 2% accuracy, with a 72% average that peaks at almost 82% for less noisy signals, which is enough for most discrete-note musical applications. The NLS-based algorithm is the most accurate one, although it is less robust to noise or input variations and requires a prior building of a model; in fact, SWIPE is one of the most employed algorithms in real-world applications not centered on speed. Additional tests are needed on real-world signals, such as validated voices of known F0, and a hardware implementation, with more parameters and selectable options is foreseen for the OBP algorithm.  
Conceptualization, D.C.; methodology, D.C. and V.C.; software, D.C. and V.C.; validation, V.C. and G.C.; formal analysis, V.C.; investigation, D.C. and V.C.; resources, V.C.; data curation, V.C.; writing--original draft preparation, D.C.; writing--review and editing, V.C.; visualization, D.C. and V.C.; supervision, G.C.; project administration, G.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The proposed dataset SYNTHPITCH of synthetic signals for testing pitch detection algorithms is available at the following link: [[https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) YP15 UljyyeIZ7k_2 wPrzWFxuig7 fbSveb?usp=sharing]([https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) YP15 UljyyeIZ7k_2 wPrzWFxuig7 fbSveb?usp=sharing) (accessed on 10 July 2023).  
The authors would like to thank Voicewise S.r.l. for the support.  
The authors declare no conflict of interest.