# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
## 3 Advancements in Knock Detection
### Audio Signals  
Knock is primarily an engine noise issue, although heavier knock, especially when sustained over long periods of time, can result in engine damage. As such, it would make sense that a direct measurement of the noise should provide a good indicator of a knocking engine. The challenge arises from the cost of audio sensors, coupled with having to detect a low-amplitude sound in a relatively noisy environment. Regardless, the human ear is well tuned for detecting engine knock; however, most audio sensors lack the same capability. As shown in Figure 6, when audio sensors are used, the signals are typically bandpass-filtered to take out the low-frequency and high-frequency noises, while maintaining the 6 kHz signal that is traditionally associated with knock.  
Figure 6 displays the different advances that have been made in detecting knock through audio signals. The advances in this area are fairly limited, since this technique is not commonly used compared to vibration and pressure sensors. Regardless, different research groups have attempted to better capture knock through audio signals, with a focus on processing.  
One novel approach was presented by [PERSON] et al. [37], who sought to use pattern recognition for knock detection, incorporating a microphone sensor, active filter, regression of the normalized envelope function, and Euclidean distance calculation. This approach actively filtered the audio signal from the engine, focusing on specific frequencies of interest. The filtered microphone data from four cycles were recorded and compared to each other to look for differences between the four cycles. If the signals were suitably different, it indicated an anomalous combustion event, and likely knock occurred in one of the cycles. The study then proceeded to use this approach to drive a fuzzy logic controller to adjust the spark timing based on the output from their algorithm. This approach showed promise in a lab setting; however, it would likely be difficult on the road, where the contribution of external noises is significantly higher.  
Figure 6: Summary of current and new advances in signal processing of audio data for detection of knock.  
Meanwhile, a study by [PERSON] et al. [38] employed a bispectrum analysis of microphone data to detect knock even in very noisy conditions. While traditional power spectrum analysis looks at the frequency contributions to a signal, bispectrum analysis also considers the phase of each frequency relative to each other. They extracted features from the bispectrum analysis that indicated knock, and then trained a model using machine learning algorithms. The results coincided well with knock experts audibly listening for knock. Furthermore, their study found that with only one microphone, they were able to differentiate between which cylinder was knocking in a multi-cylinder engine.  
[PERSON] [39] performed an analysis of using machine learning to detect engine knock, with a goal of being able to replicate a human's ability to detect knock. In particular, their study collected audio signals and processed them through Fourier transforms to determine the contribution of different frequencies to the overall audio signal. They then applied convolutional and artificial neural networks to determine if the engine was knocking. Neural networks are computational models inspired by the structure and functioning of the human brain, consisting of interconnected nodes (neurons) that are organized in layers to process and learn complex patterns from data. The study was able to achieve over an 90 percent efficiency with this approach.