How to achieve music emotional analysis and emotional recommendation through pyechonest

How to achieve music emotional analysis and emotional recommendation through pyechonest preface: Music is an art form that can resonate with emotional resonance.When we feel happy, sad, angry or relaxed, music is usually the medium of our emotional expression.Music emotional analysis aims to understand the emotions contained in music and use the information in the emotional recommendation system. Pyechonest is a popular Python library for interaction with Echo Nest API. It provides a set of functional tools for music -related tasks, including music emotional analysis.In this article, we will introduce how to use Pyechonest for music emotional analysis, and use this analysis result to build a simple emotional recommendation system. Step 1: Install Pyechonest First, make sure you have installed Python and have a PIP package manager.Then, execute the following command in the command line to install the Pyechonest: pip install pyechonest Step 2: Get the Echo Nest API key To use Echo Nest's function, you need to get an API key.Go to the Echo Nest Developer website (https://developper.echonest.com/) and register a new account.After logging in, you will find your API key on your profile page. Step 3: Connect Echo Nest API Introduce the Pyechonest Library in your Python code: python from pyechonest import config config.echo_nest_api_key = "Your API key" Replace the "Your API Key" in the above code with the API key you obtained in step 2. Step 4: Music and emotional analysis In order to analyze music and emotion, we need to choose a music and extract its emotional characteristics.The following is a simple code example. Use the Pyechonest library to obtain emotional characteristics from Echo Nest: python from pyechonest import song # Get music emotional characteristics def get_emotion(song_name, artist_name): track = song.search(artist=artist_name, title=song_name)[0] track.get_analysis() valence = track.valence arousal = track.arousal return valence, arousal In this code, the `song_name` parameter is the name of the music to be analyzed.`song.search` function is used to retrieve specific music,` track.get_analysis' function is used to obtain the emotional characteristics of the music.`Valence` and` Arousal` are the floating point number of emotional values, which represent the degree of pleasure and awakening, respectively. Step 5: Build an emotional recommendation system After understanding the emotional characteristics of music, we can use these features to build a simple emotional recommendation system.The following is an example. According to the emotional state provided by the user, it is recommended to match the music that matches its emotion: python def recommend_music(valence, arousal, threshold=0.1): recommended_songs = [] # 数据 from the emotional characteristics of all music from the database or music library music_library = get_music_library() for song_name, artist_name, song_valence, song_arousal in music_library: #At calculation of the difference between music and user emotions diff_valence = abs(valence - song_valence) diff_arousal = abs(arousal - song_arousal) # If the music emotion is small and the user's emotion is small, add it to the recommendation list if diff_valence < threshold and diff_arousal < threshold: recommended_songs.append((song_name, artist_name)) return recommended_songs In this example, `Valence` and` Arousal` are the value of the emotional state of the user.`get_music_library` function should obtain the emotional characteristics of all music from the database or music library, and then compare it with user emotions to add music with less emotional differences to the recommendation list. in conclusion: By using the Pyechonest Library, we can easily implement music emotional analysis and emotional recommendation system.These features can be used to build a personalized music recommendation platform to provide users with a music experience that matches its emotional state.In addition, you can also perform more complicated music analysis and excavation based on these emotional characteristics.I hope the guidance provided in this article can help you further explore this field.