## 4 Educational Guide
### Content-Based Audio Retrieval (Part 7)
Comparing running times of different implementations should leave a deep impression on students--an essential experience everyone should have in a computer science lecture. We test the robustness of constellation maps towards signal degradations by considering our Beatles example. To this end, we introduce overlay visualizations of constellation maps for qualitative analysis and metrics for quantitative analysis (see Figure 18 for an example). Furthermore, we provide an implementation of a matching function with tolerance parameters to account for small deviations of spectral peak positions. Again we use our modified Beatles excerpts to illustrate the behavior of the matching function under signal distortions. In particular, we demonstrate that the overall fingerprinting procedure is robust to adding noise or other sources while breaking down when changing the signal using time-scale modification or pitch shifting. The concept of indexing, as discussed in ([1], Section 7.1.4), is not covered in our FMP notebooks. For a Python implementation of a full-fledged fingerprinting system, we refer to [43]. While significant progress has been made for highly specific retrieval scenarios such as audio identification, retrieval scenarios of lower specificity still pose many challenges. Following ([1], Section 7.2), we address in the subsequent notebooks a retrieval task referred to as audio matching: given a short query audio clip, the goal is to automatically retrieve all excerpts from all recordings within a given audio database that musically correspond to the query. In this matching scenario, as opposed to classic audio identification, one allows semantically motivated variations as they typically appear in different performances and arrangements of a piece of music. Rather then using abstract features such as spectral peaks, audio matching requires features that capture musical (e.g., tonal, harmonic, melodic) properties. In the FMP Notebook Feature Design (Chroma, CENS), we consider a family of scalable and robust chroma-related audio features (called CENS), originally proposed in [44]. Using different performances of [PERSON]'s Fifth Symphony, we study the effects introduced by the quantization, smoothing, normalization, and downsampling operations used in the CENS computation. The CENS concept can be applied to various chromagram implementations as introduced in the FMP Notebook Log-Frequency Spectrogram and Chromagram of Part 3 and the FMP Notebook Template-Based Chord Recognition of Part 5. From an educational viewpoint, these notebooks should make students aware that one may change a feature's properties considerably by applying a little postprocessing. When trying out some complicated techniques, one should keep an eye on the simple and straightforward approaches (which often yield profound insights into the task and data at hand and may serve as baselines to compare against). Figure 18: Evaluation measures that indicate the agreement between two constellation maps computed for an original version (Reference) and a noisy version (Estimation). Next, the FMP **Notebook**_Diagonal Matching_ provides a step-by-step implementation of the retrieval procedure described in ([1], Section 7.2.2). Using a toy example, we discuss the matching function's behavior under signal distortions (including stretching and compressing). We then introduce a function that iteratively extracts local minima under certain neighborhood constraints. In some sense, this procedure can be regarded as a simple peak picking strategy, which should be compared with the more involved alternatives, as discussed in the FMP **Notebook**_Peak Picking_ of **Part 6**. Finally, we cover a multiple-query strategy, where we generate multiple versions of a query by applying scaling operations that simulate different tempi [45]. We illustrate the effect of this procedure by continuing our toy example from above.