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Q1:Project requirement build a causal NLP model in which I have text in one column and its corresponding label in another column. I am trying to figure out the cause of the label within the model. For example, I camped in a forest and was bitten by a mosquito and the label is Malaria. So, the cause of the label Malaria is the mosquito. Use any data set available Python or RSee Answer
Q2:Project requirement Build a causal NLP model in which I have text in one column and its corresponding label in another column. I am trying to figure out the cause of the label within the model. For example, I camped in a forest and was bitten by a mosquito and the label is Malaria. So, the cause of the label Malaria is the mosquito. Use any data set available Python or R Instructions from student You can you use pre trained model The requirement is we have a text column and a label column i want to get a causation out of the text like what was the reason of a crash happening at the the intersection Provide model with code and the detailed comments and a frequency plot of causation and a causality diagram this can help to be more clear https://arxiv.org/abs/2012.05453 Example It will be like let say you have label malaria and you have text describing being bitten by a mosquito so the cause will be bitten by a mosquito. You have a text describing a crash at intersection where label is intersection and the text is being describe the vehicle did not stop at red light so that is the causeSee Answer
Q3:You have been provided with a starter notebook that reads a collection of tweets and a collection of news articles about one particular company. Use appropriate topic modeling technique to identify top N most important topics. To get quality results apply appropriate text cleaning methods. • Present top N most important topics in the news articles and tweets o For news articles, consider how to effectively combine information from the title and text of news article • Select N to identify relevant topics, but minimize duplication • Explain how you selected N Rules and requirements: • Your final output and the code should be contained within Jupyter Notebook (ipynb) NLP Assignment 6 Starter.ipynbSee Answer
Q4: SUSS SINGAPORE UNIVERSITY OF SOCIAL SCIENCES AIB551 Natural Language Processing Group-based Assignment January 2024 Presentation [AIB551] GROUP-BASED ASSIGNMENT Group-based Assignment Note to Students: Compose your report using Microsoft Office Word, and save either as .doc or .docx (preferred). Submit the report online to Canvas via TurnItIn (for plagiarism detection) under the GBA01 submission link. Create a .Zip file that contains the presentation slides and code file (in the required format) used for data analysis. Submit the .Zip file online to Canvas via the -GBA Zip File submission link. You are to include the following particulars in your submission: Course Code, Title of the GBA, SUSS PI No., Your Name, and Submission Date. Use of Generative AI Tools (Allowed) The use of generative AI tools is allowed for this assignment. You are expected to provide proper attribution if you use generative AI tools while completing the assignment, including appropriate and discipline-specific citation, a table detailing the name of the AI tool used, the approach to using the tool (e.g. what prompts were used), the full output provided by the tool, and which part of the output was adapted for the assignment; To take note of section 3, paragraph 3.2 and section 5.2, paragraph 2A.1 (Viva Voce) of the Student Handbook; SINGAPORE UNIVERSITY OF SOCIAL SCIENCES (SUSS) Page 2 of 4 [AIB551] Group-based Assignment The University has the right to exercise the viva voce option to determine the authorship of a student's submission should there be reasonable grounds to suspect that the submission may not be fully the student's own work. For more details on academic integrity and guidance on responsible use of generative Al tools in assignments, please refer to the TLC website for more details; The University will continue to review the use of generative AI tools based on feedback and in light of developments in AI and related technologies. Question 1 Assignment title: Sentiment Analysis for Financial News Business case: A consulting firm has access to a large collection of financial news articles. They believe this treasure trove of textual information has valuable insights. They are interested in understanding the sentiment often expressed in these articles towards various businesses and leverage that sentiment analysis to make informed financial decisions. However, manual analysis of these texts is time-consuming and may not always be accurate due to the complexities of human language. This is where your recent learning about NLP can be applied. Your task is to design an NLP model that can understand, analyse and classify the sentiment expressed in these financial news articles. Tasks: Here is a dataset (https://huggingface.co/datasets/financial_phrasebank), which includes sentences from various financial news and their corresponding sentiment labels. The dataset is divided by agreement rate of 5-8 annotators. Please use 'Sentences_AllAgree.txt' which contains 2.26K rows of data for this project. (a) Design and implement relevant NLP workflow and techniques for analysing this dataset. (i) Data preprocessing: Prepare and preprocess data to ensure it is in a suitable format for the relevant algorithms. (ii) (12 marks) Feature capturing: Demonstrate the statistics of the dataset to understand the data you have, not limited to building vocabulary, vocabulary visualization (for entire data and data with certain labels), etc. SINGAPORE UNIVERSITY OF SOCIAL SCIENCES (SUSS) (15 marks) Page 3 of 4 [AIB551] Group-based Assignment (iii) Vectorization of the unstructured data: Justify whether vectorization is applicable for this sentiment analysis task and if so, select appropriate techniques and explain the rationale of the selection. (15 marks) (iv) Sentiment analysis: Select and implement model for sentimental analysis task. (v) (16 marks) Evaluation of the model: Make recommendations on which metrics are suitable for evaluating this task and evaluate the models using the identified metrics. (12 marks) (b) Conclude the results and extract business insights: Discuss the relevance of sentiment analysis in finance. How to improve the business decisions/solutions for the selected business scenarios based on the findings. (30 marks) Deliverables: • • • A Python notebook containing the code and the execution outputs of the code you have used for this task. Use headings to indicate the target questions, followed by code blocks for that question. You are highly encouraged to comment your code for better readability and ensure reproducibility of the results you get. A report illustrating the process, findings, visualizations, and recommendations. A short presentation summarizing your project. There will be a 10-minute presentation session for each group including Q&A. END OF ASSIGNMENT SINGAPORE UNIVERSITY OF SOCIAL SCIENCES (SUSS) Page 4 of 4See Answer
Q5: CSE 635: NLP and Text Mining Spring 201 Group Project Description and Requirements Overview The goal of this semester-long project is to provide hands-on experience designing, implementing, evaluating, and demonstrating a complete web mining/ text mining/ social media mining solution based on a combination of natural language processing (NLP), information retrieval (IR) and machine learning (ML) techniques. You are provided a choice of four topics that broadly fall into the AI for Social Impact area. Each project (except one) will have a standard dataset and ground truth, enabling quantitative evaluation. Many of these are from past or ongoing challenges and have been attempted by other teams. We encourage you to use any available online tools or platforms to develop your solution. You should strive to produce results in the top 10% of any previously published results on the same dataset. While there is a quantitative evaluation component in the last stage, you must develop a live demo system. This may involve developing a user interface so you can demonstrate the system. This project will satisfy the MS project requirements specified by the CSE department. While the problem definition and evaluation dataset have been fixed, there is ample room for creativity on your part in further enhancement of the solution and implementation. Be creative, and most importantly, pace yourself properly during the semester. Your project is divided into three phases, which are described in more detail later on in this document: Phase 1: Submission of project proposal and in-person presentation of your proposal. This includes a comprehensive literature review on your selected topic, a necessary step before you begin designing your system! Phase 2: Interim report describing the evaluation of the baseline system. Phase 3: Final submission of technical paper, code, and in-class presentation of your end-to-end system. Project Option 1 - LLMs in the Health Sciences Task Overview: Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT) Big Picture Researchers have initiated a challenge centered around Clinical Trial Reports (CTRs) related to breast cancer treatments to enhance how artificial intelligence interprets and utilizes medical reports. These reports are critical for medical professionals to determine the safety and efficacy of new treatments. Still, they are voluminous and complex, making it challenging for individuals to review each one thoroughly. The challenge involves the AI analyzing summaries of these reports, focusing on key aspects such as eligibility criteria, treatment specifics, trial outcomes, and observed adverse effects. Researchers crafted statements about these summaries, requiring the AI to assess whether these statements are true or false or if there's insufficient information to decide. To further test the Al's capabilities, these statements were intentionally altered by modifying numbers, changing words, or restructuring sentences. The ultimate goal is to refine the Al's consistency in understanding and ability to logically deduce information, thereby supporting medical professionals in making informed decisions about patient care. This underscores the potential of AI to significantly contribute to medical science, particularly in the realm of personalized medicine, by streamlining the interpretation and application of extensive clinical trial data. This task is based on a collection of breast cancer CTRs (extracted from https://clinicaltrials.gov/ct2/home), statements, explanations, and labels annotated by domain expert annotators. Task: Textual Entailment For the task, we have CTRs into 4 sections: • Eligibility criteria - A set of conditions for patients to be allowed to take part in the clinical trial Intervention - Information concerning the treatment type, dosage, frequency, and duration being studied. Results - Number of participants in the trial, outcome measures, units, and the results. ● Adverse events - These are signs and symptoms observed in patients during the clinical trial. The annotated statements are sentences with an average length of 19.5 tokens, that make some type of claim about the information contained in one of the sections in the CTR premise. The statements may make claims about a single CTR or compare 2 CTRs. The task is to determine the inference relation (entailment vs contradiction) between CTR - statement pairs. The training set we provide is identical to the training set used in our previous task, however, we have performed a variety of interventions on the test set and development set statements, either preserving or inversing the entailment relations. We will not disclose the technical details adopted to perform the interventions to guarantee fair competition and in the interest of encouraging approaches that are robust and not simply designed to tackle these interventions. The technical details will be made publicly available after the evaluation phase, and in our task description paper. Intervention targets • Numerical - LLMs still struggle to consistently apply numerical and quantitative reasoning. As NLI4CT requires this type of inference, we will specifically target the models' numerical and quantitative reasoning abilities. • Vocabulary and syntax - Acronyms and aliases are significantly more prevalent in clinical texts than general domain texts, and disrupt the performance of clinical NLI models. Additionally, models may experience shortcut learning, relying on syntactic patterns for inference. We target these concepts and patterns with an intervention. • Semantics - LLMs struggle with complex reasoning tasks when applied to longer premise-hypothesis pairs. We also intervene on the statements to exploit this. - Notes The specific type of intervention performed on a statement will not be available at test or training time. Dataset We will provide you with the dataset for training (which will be available in Github). Evaluation The evaluation of performance on this task will involve several steps. First, we will assess performance on the original NLI4CT statements without any interventions. This assessment will be based on Macro F1-score. Then, we will measure the Faithfulness and Consistency. (More details will be provided in the Github Repository) Bonus Points Completing all Objectives, writing a research paper, and submitting to at least a workshop/conference. Project Option 2 - LLMs in the Social Sciences Task: Multilingual Detection Of Persuasion Techniques In Memes Big Picture Imagine you're in a world where images paired with catchy text, known as memes, are not just for laughs but can also sway people's opinions and spread misinformation. These memes can be powerful on social media, reaching and influencing countless users with simple yet impactful messages. Some memes use sneaky tactics to persuade or mislead, such as making things seem simpler than they are, calling people names to discredit them, or using emotional appeals to bypass rational thinking. Technical Description We refer to propaganda whenever information is purposefully shaped to foster a predetermined agenda. Propaganda uses psychological and rhetorical techniques to reach its purpose. Such techniques include the use of logical fallacies and appealing to the emotions of the audience. Logical fallacies are usually hard to spot since the argumentation, at first sight, might seem correct and objective. However, a careful analysis shows that the conclusion cannot be drawn from the premise without the misuse of logical rules. Another set of techniques makes use of emotional language to induce the audience to agree with the speaker only on the basis of the emotional bond that is being created, provoking the suspension of any rational analysis of the argumentation. Memes consist of an image superimposed with text. The role of the image in a deceptive meme is either to reinforce/complement a technique in the text or to convey itself one or more persuasion techniques. Tasks Subtask 1 - Given only the "textual content” of a meme, identify which of the 20 persuasion techniques, organized in a hierarchy, it uses. If the ancestor node of a technique is selected, only a partial reward is given. This is a hierarchical multilabel classification problem. Subtask 2 - Given a meme, identify which of the 22 persuasion techniques, organized in a hierarchy, are used both in the textual and in the visual content of the meme (multimodal task). If the ancestor node of a technique is selected, only a partial reward will be given. This is a hierarchical multilabel classification problem. You can find info on the hierarchy below. Ad Hominem Persuasion Ethos Pathos Justification Logos Reasoning Distraction Simplification Name Calling Bandwagon Appeal to Emotion (Visual) Slogans Repetition Straw Man Causal Oversimplification Appeal to Doubt Exaggeration Authority Intentional Vagueness Red Herring Black & White Fallacy Smears Glittering Generalities Loaded Language Whataboutism Thought Terminating Cliché Reductio ad Hitlerum Flag Waving Appeal to Fear Transfer Color Legend Black = first level Red = second Level Blue third level Green fourth level White = techniques Hierarchy of the techniques for Subtask 2a (in Subtask 1 "Transfer" and "Appeal to Strong emotion" are not present). The hierarchy is also inspired by this document Dataset We will provide the dataset, more information will be on Github Evaluation Subtask 1 Subtask 1 is a hierarchical multilabel classification problem. Taking the figure above with the hierarchy as an example, any node of the DAG can be a predicted label. The gold label is always a leaf node of the DAG. If the prediction is the correct label, We use hierarchical-F1 as the official evaluation measure. Subtask 2 Subtask 2 is a hierarchical multilabel classification problem. We use hierarchical-F1 as the official evaluation measure. Bonus Points Completing all Objectives, writing a research paper, and submitting to at least a workshop/conference.See Answer
Q6:The assessment is to assess your ability to design NLP solutions based on the following case. Amazon is one of the world's largest e-commerce companies, with a vast product range. It receives hundreds of thousands of product reviews daily from its users worldwide. These reviews contain valuable information about the product and customer sentiments towards it. However, with the volume of incoming data, it's nearly impossible for Amazon to manually analyse all the reviews to extract actionable insights. This is where NLP comes in. Your task is to create an NLP solution to help Amazon structure and analyse the review data. Here is an Amazon review dataset collected in the range of May 1996 - Oct 2018, including reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). You are encouraged to use the smaller per-category dense subsets, which have been reduced to extract the k-core, such that each of the remaining users and items have k reviews each. Dataset link: https://cseweb.ucsd.edu/~jmcauley/datasets/amazon_v2//nQuestion la Based on your task, which is to create an NLP solution to help Amazon structure and analyse the review data, determine a scenario/problem that you are going to analyse and design NLP solution for the identified scenario/problem. (word limit: 500) (20 marks) Question 1b Design and implement relevant NLP analysis flow to address the identified questions, which includes but is not limited to: (i) Dataset preparation; (ii) Vocabulary building; (iii) Sentiment analysis; (iv) Named Entity Recognition; (v) Topic modelingSee Answer
Q7: SUSS SINGAPORE UNIVERSITY OF SOCIAL SCIENCES AIB551 End-of-Course Assessment - January Semester 2024 Natural Language Processing INSTRUCTIONS TO STUDENTS: 1. This End-of-Course Assessment paper comprises 9 pages (including the cover page). 2. You are to include the following particulars in your submission: Course Code, Title of the ECA, SUSS PI No., Your Name, and Submission Date. 3. Late submission will be subjected to the marks deduction scheme. Please refer to the Student Handbook for details. AIB551 Copyright © 2024 Singapore University of Social Sciences (SUSS) ECA - January Semester 2024 Page 1 of 9 ECA Submission Guidelines Please follow the submission instructions stated below: A- What Must Be Submitted You are required to submit the following TWO (2) items for marking and grading: . • A Report A.Zip File that contains the dataset used for the report and code file used for data analysis Please verify your submissions after you have submitted the above TWO (2) items. B-Submission Deadline • • • The TWO (2) items of Report and .Zip File are to be submitted by 12 noon on the submission deadline. You are allowed multiple submissions till the cut-off date for each of the TWO (2) items. Late submission of any of the TWO (2) items will be subjected to mark-deduction scheme by the University. Please refer to Section 5.2 Para 2.4 of the Student Handbook. C-How the (2) Items Should Be Submitted • The Report: submit online to Canvas via TurnItIn (for plagiarism detection) under the ECA submission link The .Zip File that contains the dataset and code file: 。 Zip the dataset and code file ○ Submit the .Zip file online to Canvas via the -ECA Zip File submission link D-Additional guidelines on file formatting are given as follows: 1. Report • Please ensure that your Microsoft Word document is generated by Microsoft Word 2016 or higher. The report must be saved in .docx format. AIB551 Copyright © 2024 Singapore University of Social Sciences (SUSS) ECA - January Semester 2024 Page 2 of 9 2. .Zip File The dataset must be saved in .csv or.json format. The dataset must be included in the .zip file. The code file must be saved in the required format. You are to include the following particulars in your submission: Course Code, Title of the ECA, SUSS PI No., Your Name, and Submission Date. E-Please be Aware of the Following: Submission in hardcopy or any other means not given in the above guidelines will not be accepted. You do not need to submit any other forms or cover sheets (e.g. form ET3) with your ECA. You are reminded that electronic transmission is not immediate. The network traffic may be particularly heavy on the date of submission deadline and connections to the system cannot be guaranteed. Hence, you are advised to submit your work early. Canvas will allow you to submit your work late but your work will be subjected to the mark-deduction scheme. You should therefore not jeopardise your course result by submitting your ECA at the last minute. It is your responsibility to check and ensure that your files are successfully submitted to Canvas. F-Plagiarism and Collusion Plagiarism and collusion are forms of cheating and are not acceptable in any form in a student's work, including this ECA. Plagiarism and collusion are taking work done by others or work done together with others respectively and passing it off as your own. You can avoid plagiarism by giving appropriate references when you use other people's ideas, words or pictures (including diagrams). Refer to the APA Manual if you need reminding about quoting and referencing. You can avoid collusion by ensuring that your submission is based on your own individual effort. The electronic submission of your ECA will be screened by plagiarism detection software. For more information about plagiarism and collusion, you should refer to the Student Handbook (Section 5.2.1.3). You are reminded that SUSS takes a tough stance against plagiarism or collusion. Serious cases will normally result in the student being referred to SUSS's Student Disciplinary Group. For other cases, significant mark penalties or expulsion from the course will be imposed. AIB551 Copyright © 2024 Singapore University of Social Sciences (SUSS) ECA - January Semester 2024 Page 3 of 9 G-Use of Generative AI Tools (Allowed) The use of generative AI tools is allowed for this assignment. • • • You are expected to provide proper attribution if you use generative AI tools while completing the assignment, including appropriate and discipline-specific citation, a table detailing the name of the AI tool used, the approach to using the tool (e.g. what prompts were used), the full output provided by the tool, and which part of the output was adapted for the assignment; To take note of section 3, paragraph 3.2 and section 5.2, paragraph 24.1 (Viva Voce) of the Student Handbook; The University has the right to exercise the viva voce option to determine the authorship of a student's submission should there be reasonable grounds to suspect that the submission may not be fully the student's own work. For more details on academic integrity and guidance on responsible use of generative AI tools in assignments, please refer to the TLC website for more details; The University will continue to review the use of generative AI tools based on feedback and in light of developments in AI and related technologies. AIB551 Copyright © 2024 Singapore University of Social Sciences (SUSS) ECA - January Semester 2024 Page 4 of 9 (Full marks: 100) Section A (100 marks) Answer all questions in this section. Question 1 The assessment is to assess your ability to design NLP solutions based on the following case. Amazon is one of the world's largest e-commerce companies, with a vast product range. It receives hundreds of thousands of product reviews daily from its users worldwide. These reviews contain valuable information about the product and customer sentiments towards it. However, with the volume of incoming data, it's nearly impossible for Amazon to manually analyse all the reviews to extract actionable insights. This is where NLP comes in. Your task is to create an NLP solution to help Amazon structure and analyse the review data. Here is an Amazon review dataset collected in the range of May 1996 - Oct 2018, including reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). You are encouraged to use the smaller per-category dense subsets, which have been reduced to extract the k-core, such that each of the remaining users and items have k reviews each. Dataset link: https://cseweb.ucsd.edu/~jmcauley/datasets/amazon_v2/ Question 1a Based on your task, which is to create an NLP solution to help Amazon structure and analyse the review data, determine a scenario/problem that you are going to analyse and design NLP solution for the identified scenario/problem. (word limit: 500) Question 1b (20 marks) Design and implement relevant NLP analysis flow to address the identified questions, which includes but is not limited to: (i) Dataset preparation; (ii) Vocabulary building; (iii) Sentiment analysis; (iv) Named Entity Recognition; (v) Topic modeling AIB551 Copyright © 2024 Singapore University of Social Sciences (SUSS) ECA - January Semester 2024 Page 5 of 9See Answer
Q8:You have been provided with a starter notebook that reads a collection of tweets and a collection of news articles about one particular company. You goals are: 1. Identify what is this company name, by looking at the entity distributions across both tweets and news articles o While analyzing news articles, extract separate entities from titles and texts 2. Identify what other companies are most frequently mentioned along with your primary company o What companies are most frequently mentioned within the same document (tweet and news article) as your primary company. 3. Identify most frequent locations of events, by extracting appropriate named entities o Locations may include countries, states, cities, regions, etc. In order to complete this analysis: • Discard non-English results Apply appropriate text cleaning methods • Within your Jupyter notebook: ○ Show a table or chart with your top-20 companies (sorted in the descending order) o You are welcome to use separate tables for titles and texts of the news articles • Use a couple of different NER packages and options, (i.e. both NLTK and SpaCy, also with and without sentence segmentation). This way you can evaluate which model provided you the best results o Your top-20 list should only be based on your most accurate results from the best performing NER package Rules and requirements: • Your final output and the code should be contained within Jupyter Notebook (ipynb) NLP Assignment 5 Starter.ipynb/nSee Answer
Q9: Intelligent Signal Processing Coursework for midterm Introduction The midterm coursework for Intelligent Signal Processing consists of a series of four individual exercises. These exercises cover the first five topics of the course: Digitising, representing, and storing audio signals - Editing and processing digital audio - - Frequency domain representations Extracting features from audio signals Speech recognition. The exercises are strongly based on the subjects covered during the course, but also invite the student for further investigation. It is recommended that the students carefully read all the sections of this document, both to ensure a good understanding of the coursework exercises, in addition to knowing what to submit. Exercise 1 Description The goal of this exercise is to create a web-based audio application using p5.js and its library p5.sound that processes a pre-recorded sound file, sending the processed audio signal to the computer's speakers or audio output. Optionally, the user could also record the processed audio signal as a digital audio file on the computer's drive. The application should include the following effects: low-pass filter, waveshaper distortion, dynamic compressor, reverb and master volume. skip skip pause play stop to start to end loop record low-pass filter dynamic compressor master volume cutoff frequency resonance attack knee release dry/wet output level ratio threshold reverb reverb duration decay rate dry/wet output level waveshaper distortion distortion amount oversample spectrum in reverse о dry/wet output level output dry/wet level 口 spectrum out Figure 1. Schema of the GUI of the application. Sound File Low Pass Filter Waveshaper Distortion Dynamic Compressor Reverb Master Volume Figure 2. Internal signal flow of the application. Regarding the pre-recorded sound file, you should record in Audacity these two lines from the poem If by Rudyard Kipling: If you can dream – and not make dreams your master; - If you can think - and not make thoughts your aim; The audio must be recorded at an optimal recording level without clipping. The recording must also be edited in Audacity, in order to remove possible silences at the beginning and end of the file. Finally, the recording must be normalised and saved as a WAV format, at 48 kHz and 24-bits. The functionality of the application should meet the following requirements: 1. The application should include the playback controls and effects controls shown in Image 1. 2. Internally, the effects must be connected in a chain, as shown in Image 2. 3. The application should include a Record button that allows the user to start/stop recording the processed signal as a WAV file. 4. The application must display both the spectrum of the original sound and the spectrum of the processed sound. Ideas for further development: 1. Enhance the filter effect by adding a type selector that allows the user to select between a low-pass, high-pass or band-pass filter. 2. Allow the user to select between the live microphone input and the pre-recorded audio file as the audio source for the application. 3. Configure a delay audio effect and add this to the audio chain before the dynamic compressor. List of deliverables For Exercise 1, you should submit in a ZIP file: • The source code of the application exercise 1. • • • • A link to the application running in a web page using the Coursera static web page function. A screencast recording demonstrating that the application meets all the requirements and shows implementation of the further developments (maximum length of two minutes). A link to the application running in a web page using the Coursera static web page function. A written report in PDF format, approximately 500 words. This report must include: A brief description of the processes of audio recording, editing, processing and saving in Audacity. This section must include at least two screenshots of Audacity showing both the original recorded voice, and the recorded voice after editing and normalising it. о о Marking criteria A brief description of the main characteristics of each effect and how they have been programmed. A brief analysis of the application discussing how the low-pass filter and the master volume effects affect the sound's spectrum. This should also be illustrated through screenshots. A brief description of the further development implemented. The screencast recording has a maximum length of two minutes, and it demonstrates that the application meets all the requirements and shows the further developments implemented. The sound file has been satisfactorily recorded, edited, processed and saved. Done? Marks 1 1 The application includes the requested playback controls, and these have been satisfactorily implemented. In particular, the Record button allows the user to record the processed audio signal in WAV format. 1 The effects have been connected in a chain. The chain is functioning properly, and the user can listen to the processed audio signal. 1 The filters have been correctly configured, and include the requested controls. 1 The written report includes a brief description of the processes of audio recording, editing, processing and saving in Audacity. 1 The written report includes a brief description of the main characteristics of each effect and how they have been programmed. 1 The written report includes a brief analysis of the application discussing how the low-pass filter and the master volume effects affect the sound's spectrum. The application includes further development. Total 1 2 10 Exercise 2 Description A famous DJ has contacted you to develop an interactive web-based application for visualising his music during its concerts. The application must be based on p5.js, p5.speech and the JavaScript audio feature extraction library Meyda. Task 1 First, to evaluate your skills, you are asked to perform the following task. The DJ sends you three sounds (Ex2_sound1.wav, Ex2_sound2.wav and Ex2_sound3.wav) and you have to select Meyda audio features that could help represent these sounds visually in an appropriate manner. For example, if the 'brightness' of one of the sounds radically changes over time, to select an audio feature that measures the brightness of this sound could be a good choice from a perspective of producing visual impact. To perform Task 1, you have to fill in the following table. You have to select three Meyda audio features for each sound and justify your selections. Sound 1 Sound 2 Meyda audio features Justification Sound 3 Task 2 The second task consists of creating the aforementioned web-based application for audio visualisation. The application (exercise 2) will use the song Kalte_Ohren_(_Remix_).mp3 (*) as an audio source. Figure 3. Idea for the audio visualisation application. You could use the image of Figure 3 as an inspiration. The visual variables could include: 1. Number of rectangles. 2. Rectangle size. 3. Rectangle fill colour. 4. Rectangle border size. 5. Rectangle border colour. 6. Rectangle fill colour opacity. 7. Rectangle border opacity. 8. Rectangle rotation. 9. Background colour. You have the full freedom to choose which audio features to use and how to map them to the visual variables.See Answer

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